Papers with Sentiment Analysis
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System (D19-3)
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| Challenge: | a portable system for weakly-supervised aspect-based sentiment extraction is presented . ABSApp is a weakly supervised aspect based sentiment analysis system . |
| Approach: | They present a portable system for weakly-supervised aspect-based sentiment extraction . ABSApp generates domain-specific aspect and opinion lexicons based on unlabeled dataset . |
| Outcome: | The proposed system is interpretable and user friendly and can be quickly and cost-effectively used across domains . it generates domain-specific aspect and opinion lexicons, edits them, and generates an aspect-based sentiment report . the system has been successfully used in movie review analysis and convention impact analysis . |
Financial Opinion Mining (2021.emnlp-tutorials)
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| Challenge: | This tutorial will provide an overview of financial opinion mining and provide research directions. |
| Approach: | This tutorial will introduce financial opinion mining and examine possible research directions. |
| Outcome: | This tutorial aims to provide an overview of financial opinion mining and figure out research directions. |
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena (C18-2)
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| Challenge: | a new version of a dictionary for the humanities provides time-variant information on word meanings and lexical emotions. |
| Approach: | They propose an extended version of JeSemE for visualizing computationally derived time-variant information on word meanings and lexical emotions. |
| Outcome: | The proposed tool combines state-of-the-art distributional semantics with a nuanced model of human emotions. |
A Time Series Analysis of Emotional Loading in Central Bank Statements (D19-51)
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| Challenge: | a recent study has found that central bankers are communicating proactively to economic agents, resulting in a rapid growth of economic literature. |
| Approach: | They examine the affective content of central bank press statements using emotion analysis . they focus on the European Central Bank and the US Federal Reserve Bank . |
| Outcome: | The results show that the ECB and the Fed have strong emotional dimensions . the authors suggest that the use of emotion analysis could reveal latent emotions . |
Being Kind Isn’t Always Being Safe: Diagnosing Affective Hallucination in LLMs (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. |
| Approach: | They propose AHaBench, a benchmark of 500 mental-health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. |
| Outcome: | The proposed model is based on 500 mental-health-related prompts with expert-informed reference responses and a 5K-instance preference dataset enabling direct preference optimization (DPO) for alignment with emotionally responsible behavior. |
Smells like Teen Spirit: An Exploration of Sensorial Style in Literary Genres (2022.coling-1)
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| Challenge: | Sensory perceptions shape how we use language and communicate. |
| Approach: | They examine the influence of sensorial language on writing style in a collection of lyrics, novels, and poetry. |
| Outcome: | The authors find that individual use of sensorial language is not a random phenomenon; choice is likely involved. |
360° Stance Detection (N18-5)
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| Challenge: | a growing epidemic of fake news and filter bubbles makes it difficult to form an unbiased, balanced opinion towards a topic. |
| Approach: | They propose a tool that aggregates news with multiple perspectives on a topic and presents them on varying spectrums ranging from support to opposition . |
| Outcome: | The proposed tool aggregates news articles with multiple perspectives on a topic and presents them on varying spectrums ranging from support to opposition . |
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles (N18-3)
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| Challenge: | Existing work on aspect extraction from reviews has focused on capturing aspects of user preferences. |
| Approach: | They propose a neural model for aspect extraction from reviews . they use a k-means baseline to extract canonical sentences of various aspects from reviews. |
| Outcome: | The proposed model performs well on two tasks. |
Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)
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| Challenge: | Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning. |
| Approach: | They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors. |
| Outcome: | The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark. |
Stylistic approaches to predicting Reddit popularity in diglossia (2021.acl-srw)
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| Challenge: | Past research has shown that style is a strong predictor of community response, but what about a diglossia? |
| Approach: | They propose to use punctuation, stopwords and part-of-speech tags to predict the popularity of a Reddit post in a diglossia in Singapore where the basilect co-exists with an acrolect . |
| Outcome: | The proposed approach combines natural language processing (NLP) techniques with punctuation, stopwords and part-of-speech tags to predict popular posts in a diglossia. |
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction (D19-62)
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Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
| Challenge: | Previously, readmission risk classifications rely on structured information, such as sociodemographic data, comorbidity codes and physiological variables. |
| Approach: | They propose to incorporate additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
| Outcome: | The proposed model incorporates topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu (2020.aacl-srw)
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| Challenge: | Recent work on ABSA in Urdu language has limitations. |
| Approach: | They propose to create a dataset for Aspect Based Sentiment Analysis in Urdu language which will support multiple aspects. |
| Outcome: | The proposed dataset will provide a baseline evaluation for ABSA systems in Urdu language. |
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for aspect sentiment analysis do not include explicit sentiment expressions. |
| Approach: | They propose to construct a heterogeneous graph by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four benchmark datasets and significantly boosts performance in comparison with BERT. |
ASAD: Arabic Social media Analytics and unDerstanding (2021.eacl-demos)
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| Challenge: | Currently, there are no publicly available tools for analyzing Arabic social media, such as ADIDA and CAMeL, which are not trained with Twitter data. |
| Approach: | They propose to use Arabic social media analysis and unDerstanding to analyze tweets using a web API and a user interface. |
| Outcome: | The proposed system allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets. |
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)
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| Challenge: | Existing sentiment analysis models lack temporal information to capture semantics of long texts. |
| Approach: | They propose a framework to exploit task-related discourse structures for sentiment analysis. |
| Outcome: | The proposed framework improves the performance even beyond existing approaches based on human annotated data. |
Dual-Channel Span for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Existing approaches to extract sentiment triplets are too noisy and enumerate all possible spans. |
| Approach: | They propose a dual-channel span generation method to constrain the search space of span candidates. |
| Outcome: | The proposed method reduces span enumeration by nearly half on two versions of public datasets. |
BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases (2023.acl-short)
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| Challenge: | Energy-based models (EBMs) have gained popularity for controlled text generation due to their high applicability to a wide range of constraints. |
| Approach: | They propose a language model with tunable biases to adjust the language model’s output logits. |
| Outcome: | The proposed model maintains the generator’s autoregressive nature to assert a strong control on token-wise conditional dependencies and overall fluency, and converges faster. |
Designing an Automatic Agent for Repeated Language–based Persuasion Games (2022.tacl-1)
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| Challenge: | Existing work on persuasion games assumes communication with stylized messages that do not consist of real-world natural language. |
| Approach: | They propose to use a repeated sender-decision maker game to persuade a receiver to accept a deal by sending one of several possible natural language reviews to the expert. |
| Outcome: | The proposed expert is superior to baselines and adaptable to different decision makers and potential proposed deals. |
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021.emnlp-main)
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| Challenge: | Existing methods for aspect category sentiment analysis do not necessarily occur in a sentence. |
| Approach: | They propose a Beta Distribution-guided aspect-aware graph construction based on external knowledge . they use aspect-related words as the pivots to derive aspect-relevant weights . |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on 6 benchmark datasets. |
Sensing and Learning Human Annotators Engaged in Narrative Sensemaking (N18-4)
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| Challenge: | a substantial sector of the gig economy is the use of crowdworkers to annotate data for machine learning and analysis. |
| Approach: | They propose a narrative-sorting annotation task that sorts tweets chronologically by topic, emotional content, and length. |
| Outcome: | The proposed task enables readers to sort sequential, target-topical, and emotionally emotional tweets. |
DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for pre-training can be sub-optimal in some cases . for example, aspect extraction tasks require domain and category invariant representations . |
| Approach: | They propose a domain-invariant learning scheme for BERT to fine-tune pre-trained language models on a source domain and then apply it to a different target domain. |
| Outcome: | The proposed scheme improves performance over state-of-the-art models while using fraction of the unlabeled data. |
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection (2022.findings-naacl)
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| Challenge: | Existing approaches to detect novel intents have been tested in the last decade. |
| Approach: | They propose a framework to detect multiple novel intents with budgeted human annotation cost. |
| Outcome: | The proposed framework outperforms baseline methods in terms of accuracy and F1-score on a set of benchmark datasets. |
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis (2021.naacl-industry)
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| Challenge: | Existing methods for sentiment analysis are difficult to assess for erroneous predictions that might exist prior to deployment. |
| Approach: | They propose a framework for error detection based on explainable features that can detect erroneous model predictions on unseen data with high precision. |
| Outcome: | The proposed framework detects erroneous model predictions on unseen data with high precision, given limited human-in-the-loop intervention, and can be deployed on unselected data with a high accuracy. |
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text (2022.lrec-1)
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| Challenge: | Social distancing orders are the most effective strategy to reduce the spread of COVID-19 in 2020 . |
| Approach: | They propose to use NLP methods in a causal mediation scenario to emphasize the use of NLP and economics to decouple the effect of government restrictions on mobility from the effect that occurs due to public perception of the COVID-19 strategy. |
| Outcome: | The proposed model decouples the effect of government restrictions on mobility behavior from the effect that occurs due to public perception of the COVID-19 strategy in a country. |
“Stupid robot, I want to speak to a human!” User Frustration Detection in Task-Oriented Dialog Systems (2025.coling-industry)
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Mireia Hernandez Caralt, Ivan Sekulic, Filip Carevic, Nghia Khau, Diana Nicoleta Popa, Bruna Guedes, Victor Guimaraes, Zeyu Yang, Andre Manso, Meghana Reddy, Paolo Rosso, Roland Mathis
| Challenge: | Detecting user frustration in task-oriented dialog systems is imperative for maintaining overall user satisfaction, engagement and retention. |
| Approach: | They compare out-of-the-box methods for user frustration detection with open-source methods . they find an LLM-based approach is promising, as it captures both emotion and dialog breakdowns . |
| Outcome: | The proposed method outperforms open-source methods in detecting user frustration in a TOD system. |
Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge (2025.acl-long)
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| Challenge: | Existing methods for hyperbole and metaphor detection focus on superficial text features, ignoring the associations of hyperbola and metaphor . Existing frameworks focus on identifying superficial text, focusing on superficial features . |
| Approach: | They propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction. |
| Outcome: | The proposed framework outperforms baseline methods on four datasets. |
EduPulse: A Practical LLM-Enhanced Opinion Mining System for Vietnamese Student Feedback in Educational Platforms (2026.eacl-industry)
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| Challenge: | EduPulse is a system designed specifically to analyze student feedback in Vietnamese. |
| Approach: | They propose a system that analyzes student feedback in Vietnamese to improve opinion mining. |
| Outcome: | The proposed system performs four opinion analysis tasks in Vietnamese . it is scalable and maintainable, and it is cost-effective, the authors show . |
Prompt Tuning with Contradictory Intentions for Sarcasm Recognition (2023.eacl-main)
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| Challenge: | Recent advances have shown that Pre-trained Language Models (PLMs) can achieve promising performance in many downstream Natural Language Processing (NLP) tasks. |
| Approach: | They propose to incorporate prior knowledge about contradictory intentions into prompt tuning for sarcasm recognition by mimicking the actual intention by verbalizer engineering. |
| Outcome: | The proposed model mimics the actual intention by prompt construction and indicates whether the actual intent contradicts the literal content by verbalizer engineering. |
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing approaches to improve performance of deep neural models are limited by the nature of spurious patterns in the data. |
| Approach: | They propose to use augmented data to generate spurious patterns in NLP models . they propose to generate counterfactual data for data augmentation and explanation . |
| Outcome: | The proposed approach improves performance on augmented data and on human-generated data. |
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys (2021.emnlp-main)
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Kenneth Joseph, Sarah Shugars, Ryan Gallagher, Jon Green, Alexi Quintana Mathé, Zijian An, David Lazer
| Challenge: | Existing stance detection methods have been evaluated in comparison to the public opinion data they promise to replace. |
| Approach: | They propose to compare an individual's self-reported stance to the stance inferred from their social media data. |
| Outcome: | The proposed models are compared to a public opinion survey with 1,129 individuals across four salient targets. |
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond (2022.lrec-1)
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| Challenge: | Language models are ubiquitous in NLP, but current analyses focus on (multilingual variants of) standard benchmarks and task-specific corpora as multilingual signals. |
| Approach: | They propose a model to train and evaluate multilingual language models in Twitter using a set of Twitter datasets in eight different languages and a XLM-T model. |
| Outcome: | The proposed model trains and evaluates multilingual models on Twitter. |
Enhancing Self-Attention via Knowledge Fusion: Deriving Sentiment Lexical Attention from Semantic-Polarity Scores (2024.starsem-1)
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| Challenge: | Existing methods to inject lexical features into self-attention mechanisms have shown remarkable performance across various downstream tasks in NLP. |
| Approach: | They propose to inject lexical features into the self-attention mechanism of Transformer-based models by injecting lexicon-based Sentiment Lexical Attention into the attention scores throughout the training process. |
| Outcome: | The proposed method shows significant performance improvements on the NSMC sentiment classification benchmark and is able to perform in out-of-domain tasks. |
T-MAD: Target-driven Multimodal Alignment for Stance Detection (2025.emnlp-main)
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| Challenge: | Existing methods for Multimodal Stance Detection struggle with generalizing to unseen targets and handling modality inconsistencies. |
| Approach: | They propose a multimodal stability detection model which captures target-specific relationships and balances modality contributions by iterative reasoning. |
| Outcome: | Experiments on the MMSD and MultiClimate datasets show that the proposed model outperforms state-of-the-art models with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. |
Emotion Transfer with Enhanced Prototype for Unseen Emotion Recognition in Conversation (2025.emnlp-main)
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| Challenge: | Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications. |
| Approach: | They propose a prototype-based emotion transfer framework that can be used in real-world applications. |
| Outcome: | The proposed framework shows promise but still faces key challenges in the field of emotion recognition in conversation. |
LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)
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| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
| Approach: | They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics. |
| Outcome: | The proposed model is able to detect stance labels in zero-shot and cross-target scenarios. |
TweeNLP: A Twitter Exploration Portal for Natural Language Processing (2021.acl-demo)
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| Challenge: | Currently, Twitter curates 19,395 tweets from various NLP conferences and general NLP discussions. |
| Approach: | They propose to integrate tweets pertaining to research papers with the NLPExplorer scientific literature search engine to organize Twitter's natural language processing data. |
| Outcome: | The proposed system curates 19,395 tweets from various NLP conferences and general discussions. |
Unsupervised Aspect-Level Sentiment Controllable Style Transfer (2020.aacl-main)
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| Challenge: | Unsupervised style transfer has been explored in text. |
| Approach: | They propose a system where aspect-level sentiments can be controlled at the output . they propose to use unsupervised techniques such as ABSA masked-language-modelling . |
| Outcome: | The proposed system is successful in controlling aspect-level sentiments. |
It’s not Rocket Science: Interpreting Figurative Language in Narratives (2022.tacl-1)
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| Challenge: | Existing text representations by design rely on compositionality, while figurative language is often non-compositional. |
| Approach: | They propose to use a pre-trained language model to interpret figurative language types to adopt human strategies for interpreting figurativ language types: inferring meaning from context and relying on constituent words’ literal meanings. |
| Outcome: | The proposed models perform significantly worse than humans on discriminative and generative tasks, bridging the gap from human performance. |
Intent Detection with WikiHow (2020.aacl-main)
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| Challenge: | Existing approaches to intent detection have limited data annotated for new domains or languages. |
| Approach: | They propose to train a set of pretraining intent detection models on wikiHow which can predict a broad range of intended goals from many actions. |
| Outcome: | The proposed models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. |
A Closer Look at Few-Shot Out-of-Distribution Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for few-shot out-of-distribution (OOD) intent detection are not adequate . despite its importance, few- shot OOD intent detection is a challenging problem . |
| Approach: | They propose a latent representation generation and self-supervision approach to solve few-shot OOD intent detection problem. |
| Outcome: | The proposed approach is highly effective and could improve state-of-the-art methods for few-shot OOD intent detection. |
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations (2021.naacl-industry)
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| Challenge: | Intent detection models require large amounts of labeled data to achieve high accuracy, and in practical scenarios it is more common to find small, unbalanced, and noisy datasets. |
| Approach: | They benchmark intent detection methods on a variety of datasets and found that Watson Assistant's model outperforms other commercial solutions. |
| Outcome: | The proposed model outperforms pretrained language models on a variety of datasets while requiring only a fraction of computational resources and training data. |
FAST: Fast Annotation tool for SmarT devices (2021.emnlp-demo)
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| Challenge: | In real-world applications, annotators with the same attributes are required to annotate whether the outputs of natural language generation systems are fluent or not. |
| Approach: | They propose an annotation tool for application tasks that focuses on the user experience of mobile devices and can be customized to fit various tasks. |
| Outcome: | The proposed tool can annotate faster than existing methods while maintaining the quality of annotation. |
Knowledge-Enriched Two-Layered Attention Network for Sentiment Analysis (N18-2)
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| Challenge: | Existing sentiment analysis systems are prone to word shortening, exaggeration, lack of grammar and appropriate punctuation. |
| Approach: | They propose a two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis using the Knowledge Graph Embedding generated using the WordNet. |
| Outcome: | The proposed model outperforms the state-of-the-art system on the benchmark dataset of SemEval 2017 Task 5 by 1.7 and 3.7 points respectively. |
Medical Entity Corpus with PICO elements and Sentiment Analysis (L18-1)
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| Challenge: | In this paper, we establish a PICO and a sentiment annotated corpus of clinical trial publications. |
| Approach: | They propose to create a phrase-level PICO corpus and a sentence-level sentiment annotated corpus from clinical trial publications. |
| Outcome: | The proposed corpus is annotated on a phrase-level and a sentiment annotation on the same corpus. |
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (N18-2)
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| Challenge: | Recent work on target-dependent biLSTMs has shown that they are ineffective in aspect-based sentiment analysis. |
| Approach: | They propose a novel architecture that uses external memory chains with a delayed memory update mechanism to track entities. |
| Outcome: | The proposed model improves on a TABSA task using external memory chains with a delayed memory update mechanism. |
Measuring the Effect of Influential Messages on Varying Personas (2023.acl-short)
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| Challenge: | a new task estimates the response a persona might have upon seeing a news message . a first benchmark dataset is used to evaluate the performance of the proposed task . |
| Approach: | They propose a task to estimate the response a persona might have upon seeing a news message. |
| Outcome: | The proposed task estimates the response a persona might have upon seeing a news message. |
Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis (2025.acl-short)
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| Challenge: | Aspect-based sentiment analysis (ABSA) assesses sentiments towards aspects within texts, resulting in detailed sentiment tuples. |
| Approach: | They propose a dynamic order template method that dynamically creates an order template that contains only the necessary views for each instance. |
| Outcome: | The proposed method improves F1 scores on ASQP and ACOS datasets while significantly reducing inference time. |
Towards Multi-label Unknown Intent Detection (2022.coling-1)
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| Challenge: | Existing methods for multi-class unknown intent detection assume that each utterance has only one intent, which is not true because utterrances often contain multiple intents. |
| Approach: | They propose a task to detect whether an utterance contains the unknown intent by recognizing whether all intents contained in the utterant are known. |
| Outcome: | The proposed method significantly reduces the FPR95 on the MultiWOZ 2.3 dataset by 12.25% compared to the best baseline. |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
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| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis (P19-1)
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| Challenge: | Experimental results show that our proposed approach yields better attention mechanisms . dominant ASC models are mostly discriminative classifiers based on manual feature engineering . |
| Approach: | They propose a self-supervised approach to aspect-level sentiment classification that mines useful attention supervision information from a training corpus to refine attention mechanisms. |
| Outcome: | The proposed approach yields better attention mechanisms on multiple datasets. |
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (P19-1)
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| Challenge: | Existing algorithms address aspect term extraction and aspect sentiment classification as separate tasks, which can be complicated for real applications. |
| Approach: | They propose a dual crOss-sharEd RNN framework to generate all aspect term-polarity pairs of the input sentence simultaneously. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks on three benchmark datasets. |
Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification (2024.naacl-short)
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| Challenge: | Existing studies on metaphors have focused on a small number of examples, whereas few studies verify claims with large corpus. |
| Approach: | They propose to use a large corpus to verify existing claims about verb metaphors . they apply metaphor detection to sentences extracted from Common Crawl . |
| Outcome: | The proposed method identifies verb metaphors with lower concreteness, imageability, familiarity and more emotional and subjective sentences. |
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (2021.naacl-main)
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| Challenge: | Recent research shows promising results by jointly learning of slot filling and intent detection tasks. |
| Approach: | They propose a way to combine slot filling and slot filler learning to achieve state-of-the-art results. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets and ATIS datasets. |
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level. |
| Approach: | They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three widely used benchmarks. |
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian (C18-1)
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| Challenge: | RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
| Approach: | They propose to use RuSentiment to annotate social media posts in Russian with a kappa of 0.58 and a set of annotation guidelines that are extensible to other languages. |
| Outcome: | The proposed dataset is the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
Modeling and Prediction of Online Product Review Helpfulness: A Survey (P18-1)
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| Challenge: | review helpfulness modeling is a task that studies the mechanisms that affect review helpfuliness and attempts to accurately predict it. |
| Approach: | This paper provides an overview of the most relevant work in helpfulness prediction . it discusses the insights gained from said work and provides guidelines for future research . |
| Outcome: | This paper summarizes the most relevant work in helpfulness prediction and understanding in the past decade . it outlines the insights gained from the results and provides guidelines for future research . |
On the Impact of Temporal Representations on Metaphor Detection (2022.lrec-1)
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| Challenge: | State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using neural networks. |
| Approach: | They propose to use temporal and static word embeddings to account for different representations of literal meanings to examine metaphor detection tasks. |
| Outcome: | The proposed method outperforms static methods but may provide representations of the core meaning of the metaphor too close to their contextual meaning, causing confusion. |
A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis (C18-1)
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| Challenge: | Existing approaches to model aspect-level sentiment analysis neglect the position information of aspect terms. |
| Approach: | They propose a position-aware bidirectional attention network (PBAN) that mutually models the relation between aspect terms and sentences by employing bidirectional mechanism. |
| Outcome: | The proposed model can distinguish sentiment polarity of aspect terms in sentences . it can also model relation between aspect terms and sentences based on position information . |
A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives (2022.coling-1)
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| Challenge: | figurative language research has focused on sarcasm and irony, but there is still a gap in the field. |
| Approach: | They propose to review computational irony, cognitive science, and neural models of irony processing . they aim to encourage a balanced and equal research environment in figurative languages . |
| Outcome: | The proposed multi-X irony processing perspectives will provide an overview of computational irony, insights from linguisic theory and cognitive science, and interactions with downstream NLP tasks. |
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020.coling-main)
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| Challenge: | Aspect-based sentiment analysis studies focus on identifying sentiment polarities toward explicit aspects but ignore implicit aspects in text. |
| Approach: | They propose a hierarchy-sentiment hierarchy prediction problem to capture explicit and implicit aspects of aspect-based sentiment analysis. |
| Outcome: | The proposed model can consistently achieve the best results on four benchmarks. |
Constituency Lattice Encoding for Aspect Term Extraction (2020.coling-main)
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| Challenge: | a challenge for aspect term extraction is to extract phrase-level aspect terms . a constituency lattice structure is constructed using the span annotations of constituents of a sentence . |
| Approach: | They propose to incorporate the span annotations of constituents of a sentence to leverage syntactic information in neural network models. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets. |
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability (2021.findings-emnlp)
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| Challenge: | Existing methods to model multi-modal sarcasm and sentiment are based on quantum probability . sarcasm and feelings embody intrinsic uncertainty of human cognition . |
| Approach: | They propose a quantum probability-driven multi-task learning framework for sarcasm and sentiment recognition using quantum superpositions and quantum interference. |
| Outcome: | The proposed model achieves state-of-the-art in multi-modal sarcasm and sentiment recognition. |
A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications (2024.acl-long)
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| Challenge: | Historical linguists have identified multiple forms of lexical semantic change. |
| Approach: | They propose a framework for integrating and evaluating lexical semantic changes in historical linguists and a unified computational methodology for evaluating them concurrently. |
| Outcome: | The proposed framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. |
Investigating Human Values in Online Communities (2025.naacl-long)
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| Challenge: | Existing value frameworks struggle with sample sizes and rely on selfreported surveys to calculate values. |
| Approach: | They propose a method to computationally analyse values on Reddit using in-domain and out-of-domain human annotations to train a value relevance and a polarity classifier. |
| Outcome: | The proposed method can be used to analyse values on reddit using human annotations and human annotation. |
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |
Adaptive Structure Induction for Aspect-based Sentiment Analysis with Spectral Perspective (2023.findings-emnlp)
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| Challenge: | incorporating structure information can enhance the performance of aspect-based sentiment analysis. |
| Approach: | They propose to use pre-trained language models to induct latent structures from a spectrum perspective. |
| Outcome: | The proposed model shortens Aspects-sentiment Distance and improves structure induction ability. |
Transitive Consistency Constrained Learning for Entity-to-Entity Stance Detection (2024.acl-long)
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| Challenge: | Entity-to-entity stance detection is a streamlined task without the complex dependency structure for structural sentiment analysis. |
| Approach: | They propose a method that models transitive consistency constraints during training to help train entity-to-entity stance detection models. |
| Outcome: | The proposed method improves both classification-based and generation-based models while large language models struggle with predicting link direction and neutral labels. |
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain (2020.coling-main)
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| Challenge: | Existing datasets in automotive domain cover only three languages due to high cost of human annotation. |
| Approach: | They build large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. |
| Outcome: | The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. |
Exploiting contextual information to improve stance detection in informal political discourse with LLMs (2025.acl-srw)
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| Challenge: | Political stance detection is an increasingly relevant part of analyzing the flow of ideas in online environments where discourse is informal and implicitly expressed. |
| Approach: | They evaluate large language models for political stance detection in informal online discourse by analyzing user profiles derived from historical posts. |
| Outcome: | The proposed model improves accuracy by up to 74% on a political forum dataset. |
Intent Recognition in Doctor-Patient Interviews (2020.lrec-1)
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| Challenge: | Currently, up to 20 percent of patients are misdiagnosed in medical training programs. |
| Approach: | They propose to annotate doctor-patient interviews with intent inventory and information retrieval methods that are robust with respect to small amounts of training data. |
| Outcome: | The proposed models provide baseline performance scores on the data set for further research. |
Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)
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| Challenge: | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. |
| Approach: | They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity. |
| Outcome: | The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated. |
Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing (2024.findings-emnlp)
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| Challenge: | Psychological studies aim at explaining internal mechanisms of emotions, while computational studies simplify them into labels. |
| Approach: | They propose to treat emotions as strategies to cope with salient situations . they introduce a task of coping identification and a corpus constructed via role-playing . |
| Outcome: | The proposed method allows to investigate the link between emotions and behavior, which also emerges in language. |
Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on IMDb, Yelp and Longformer benchmarks. |
Reasoning with Sarcasm by Reading In-Between (P18-1)
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| Challenge: | Sarcasm is a figurative speech act which manifests on social networks such as Twitter and Reddit. |
| Approach: | They propose a model that looks in-between rather than across to explicitly model contrast and incongruity. |
| Outcome: | The proposed model achieves state-of-the-art performance on all datasets and improves interpretability. |
A Survey on Stance Detection for Mis- and Disinformation Identification (2022.findings-naacl)
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| Challenge: | Understanding attitudes expressed in texts plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentional false information). |
| Approach: | They examine the relationship between stance detection and mis- and disinformation detection online and examine the results of previous studies. |
| Outcome: | The proposed task is a component of fact-checking, rumour detection, and detecting previously fact- checked claims, and is compared with other related tasks such as argumentation mining and sentiment analysis. |
Disambiguation of Verbal Shifters (L18-1)
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
| Approach: | They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon. |
| Outcome: | The proposed approach takes into account various features, particularly generalization features. |
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis (C18-1)
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| Challenge: | a lack of reproducibility and generalisability is a major threat to scientific development in Natural Language Processing. |
| Approach: | They propose to use a model zoo to document and release language models and published code . they recommend that future replication experiments should consider a variety of datasets . |
| Outcome: | The proposed methods are compared on six English datasets and are based on the results. |
Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation (2025.acl-srw)
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| Challenge: | Existing datasets that are outdated and inaccurate hinder accuracy of Financial Sentiment Analysis (FSA) . |
| Approach: | They propose a data augmentation technique using Retrieval Augmented Generation (RAG) to infuse established benchmarks with up-to-date contextual information from contemporary financial news. |
| Outcome: | The proposed method modernizes established benchmarks with up-to-date contextual information while addressing class imbalances. |
A Large Self-Annotated Corpus for Sarcasm (L18-1)
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| Challenge: | Existing datasets for sarcasm detection have unbalanced and self-annotated labels, allowing for learning in both balanced and unbalanciated label regimes. |
| Approach: | They introduce the Self-Annotated Reddit Corpus (SARC) which has 1.3 million sarcastic statements and many times more instances of non-sarcasm statements. |
| Outcome: | The proposed corpus has 1.3 million sarcastic statements and many more instances of non-sarcasm statements, allowing for learning in both balanced and unbalanced label regimes. |
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions (P19-1)
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| Challenge: | Existing peer review system is not straightforward and requires domain knowledge, expertise, and intelligence of human reviewers, which is somewhat elusive with the current state of AI. |
| Approach: | They propose to use peer review texts to predict acceptance or rejection of a manuscript based on reviewer sentiment. |
| Outcome: | The proposed deep neural architecture achieves significant performance improvement over baselines (29% error reduction) in a recently released dataset of peer reviews. |
From Polarity to Intensity: Mining Morality from Semantic Space (2022.coling-1)
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| Challenge: | Existing approaches to compute moral intensity are limited to word-level measurement and heavily rely on human labelling. |
| Approach: | They propose a weakly-supervised framework that can automatically measure moral intensity from text. |
| Outcome: | The proposed framework can measure moral intensity from text with moral polarity labels, which are more robust and easier to acquire. |
EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian (2025.findings-emnlp)
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| Challenge: | **EmoBench-UA** is the first annotated dataset for emotion classification in Ukrainian texts. |
| Approach: | They introduce **EmoBench-UA**, the first annotated dataset for emotion detection in Ukrainian texts. |
| Outcome: | The first annotated dataset for emotion detection in Ukrainian texts is presented in this paper . the dataset was created through crowdsourcing using the Toloka.ai platform . |
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection (P19-1)
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| Challenge: | Past work in personal health mention detection uses classification-based methods with human-engineered features or word embedding-based features. |
| Approach: | They propose to combine a pipeline-based and a feature augmentation-based approach to combine personal health mention detection with figurative usage detection to improve the accuracy of the prediction. |
| Outcome: | The proposed method improves the F-score of personal health mention detection by 2.21% over the pipeline-based approach and feature augmentation-based approaches. |
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions (C18-1)
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| Challenge: | Emotion analysis and classification is a challenging task which has been tackled with relatively straight-forward approaches. |
| Approach: | They propose to annotate emotion trigger phrases and entities in the roles of experiencers, targets, and causes of the emotion in literature by Project Gutenberg. |
| Outcome: | The proposed corpus supports qualitative literary studies and digital humanities. |
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)
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| Challenge: | Existing methods for detecting intended sarcasm have shown low performance compared to previous studies. |
| Approach: | They propose a dataset of tweets labeled for intended sarcasm by their authors . they aim to encourage future NLP research to develop methods for detecting sarkasmus in text as intended by the authors of the text . |
| Outcome: | The proposed model shows that existing methods are biased or obvious and sarcasm could be understudied. |
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)
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| Challenge: | Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge. |
| Approach: | They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms. |
| Outcome: | The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors. |
Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
| Approach: | They use Twitter stance data to collect stances on topics related to the pandemic . they train models to take advantage of large amounts of unlabeled data . |
| Outcome: | The proposed model improves on existing stance detection datasets and unlabeled data. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
Rethinking Sentiment Style Transfer (2021.findings-emnlp)
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| Challenge: | Existing evaluation methods for text style transfer are unsatisfactory. |
| Approach: | They propose to use a graph-based method to extract attribute content from sentences . they propose an efficient regularization to leverage attribute-dependent content as guiding signals. |
| Outcome: | The proposed method is based on a YELP and IMDB dataset and it is able to detect errors in the human evaluation. |
Learning Sentiment Memories for Sentiment Modification without Parallel Data (D18-1)
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| Challenge: | Existing methods for sentiment modification generate input-irrelevant texts due to lack of parallel data. |
| Approach: | They propose a method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. |
| Outcome: | The proposed method significantly improves the content preservation degree and achieves the state-of-the-art performance. |
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
| Outcome: | The proposed model outperforms pipeline approaches in aspects-based sentiment analysis. |
Representing Social Media Users for Sarcasm Detection (D18-1)
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
| Outcome: | The proposed approach performs better in homogeneous contexts, whereas the dense embeddings prove valuable in more diverse contexts. |
CMU-MOSEAS: A Multimodal Language Dataset for Spanish, Portuguese, German and French (2020.emnlp-main)
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AmirAli Bagher Zadeh, Yansheng Cao, Simon Hessner, Paul Pu Liang, Soujanya Poria, Louis-Philippe Morency
| Challenge: | Existing datasets in multimodal language are limited and disproportionately affect native speakers of other languages . authors propose a large-scale dataset for Spanish, Portuguese, German and French . |
| Approach: | They propose a large-scale multimodal language dataset for Spanish, Portuguese, German and French. |
| Outcome: | The proposed dataset is the largest of its kind with 40,000 total labelled sentences . it covers a diverse set topics and speakers and carries supervision of 20 labels including sentiment, emotions, and attributes. |
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning (2021.emnlp-main)
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Jianguo Zhang, Trung Bui, Seunghyun Yoon, Xiang Chen, Zhiwei Liu, Congying Xia, Quan Hung Tran, Walter Chang, Philip Yu
| Challenge: | Existing methods address few-shot intent detection tasks from two perspectives: data augmentation and task-adaptive training with pre-trained models. |
| Approach: | They propose a few-shot intent detection schema using contrastive pre-training and fine-tuning. |
| Outcome: | The proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings. |
Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa (2021.naacl-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a fine-grained task in sentiment analysis. |
| Approach: | They compare a model with a dependency parser and a tree from a fine-tuned RoBERTa model to find the polarities for aspects in a sentence. |
| Outcome: | The proposed model outperforms the parser-provided tree on six datasets across four languages. |
Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis (2025.coling-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. |
| Approach: | They propose a method to enhance long-range dependencies between aspect and opinion words in ABSA by combining attention mechanisms with a syntax-based Graph Convolutional Network and a Mamba-Transformer module. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark datasets. |
Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Existing sentiment analysis models treat aspects and targets separately, causing conflicting sentiments. |
| Approach: | They propose an approach that jointly considers aspects and targets when inferring sentiments. |
| Outcome: | The proposed approach outperforms leading models by 1.6% to 4.3% on benchmark datasets . it uses selective attention mechanisms for selective attention between targets and context words . |
DomBERT: Domain-oriented Language Model for Aspect-based Sentiment Analysis (2020.findings-emnlp)
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| Challenge: | Recent studies show that learning domain-specific language models are equally important for general-purpose and domain-based learning. |
| Approach: | They propose a domain-oriented learning task that combine the benefits of both general and domain-specific worlds. |
| Outcome: | The proposed task solves the problems in an aspect-based sentiment analysis task. |
Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. |
| Approach: | They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples. |
| Outcome: | The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision. |
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)
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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan
| Challenge: | Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment. |
| Approach: | They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task. |
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)
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| Challenge: | Sentiment analysis is a key task in e-commerce to detect fine-to-coarse sentiment polarities. |
| Approach: | They propose to use a large-scale Chinese restaurant review dataset ASAP to investigate the sentiment polarities underlying user reviews. |
| Outcome: | The proposed model outperforms state-of-the-art models on both tasks. |
Zero-Shot Conversational Stance Detection: Dataset and Approaches (2025.findings-acl)
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| Challenge: | Existing stance detection datasets are limited to a limited set of specific targets . current models are limited in their ability to detect large numbers of unseen targets based on a large number of unidentified targets. |
| Approach: | They propose a speaker interaction and target-aware prototypical contrastive learning model that can detect public opinion towards specific targets using social media data. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot conversational stance detection with only an F1-macro score of 43.81%. |
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots (2021.findings-emnlp)
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| Challenge: | Existing work on retrieval-based chatbots has low-quality affect response . Existing frameworks for obtaining affective response are based on Retrieve-and-Rerank . |
| Approach: | They propose a retrieval-based framework which provides affective response for retrieval chatbots by using a new discriminate-and-rewrite mechanism. |
| Outcome: | The proposed framework outperforms existing baselines and can guarantee the quality of the response and satisfy the affect label. |
WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations (2021.naacl-main)
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| Challenge: | Existing studies on emotion analysis use subjective emotional intensity labels by the writers and objective ones by the readers. |
| Approach: | They annotate 17,000 SNS posts with both the writer's subjective emotional intensity and the reader's objective emotional intensity to construct a Japanese emotion analysis dataset. |
| Outcome: | The results show that the reader cannot fully detect the emotions of the writer, especially anger and trust. |
Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations (D18-1)
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| Challenge: | a lack of datasets distinguish between conventionalized and novel metaphors is limiting research . a novel metaphor is often overlooked or intentionally disregarded, authors say . |
| Approach: | They propose a crowdsourced annotation layer for an existing metaphor corpus to investigate novelty . they investigate correlations between concreteness ratings and more semantic features . |
| Outcome: | The proposed method combines novel metaphor annotations with concreteness ratings and semantic features. |
Multi-Condition Guided Diffusion Network for Multimodal Emotion Recognition in Conversation (2025.findings-naacl)
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| Challenge: | Current research emphasizes contextual factors, the speaker’s influence, and extracting complementary information across different modalities. |
| Approach: | They propose a diffusion-based approach to address the challenges posed by redundant information and redundant information at the semantic level while robustly capturing shared semantics. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on two multimodal datasets and is generalizable and effective. |
Transition-based Opinion Generation for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Existing approaches to aspect-based sentiment analysis often overlook the importance of explicitly modeling structure among sentiment elements. |
| Approach: | They propose to integrate general pre-trained sequence-to-sequence language models with a structure-aware transition-based approach to model sentiment structure. |
| Outcome: | The proposed model improves the state-of-the-art performance on several benchmark datasets. |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)
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| Challenge: | Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. |
| Approach: | They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach. |
| Outcome: | The proposed model improves performance on multiple datasets and compares with existing models. |
MuSE: a Multimodal Dataset of Stressed Emotion (2020.lrec-1)
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| Challenge: | Existing studies on the effects of stress and emotion on the production and perception of emotion are understudied. |
| Approach: | They propose to use a multimodal stressed emotion dataset to study the interplay between the presence of stress and expressions of affect. |
| Outcome: | The proposed dataset combines emotion and stress classification with annotations for the emotional content of the recordings. |
RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims (2025.findings-naacl)
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| Challenge: | Social media provides a valuable lens for assessing public perceptions and opinions. |
| Approach: | They propose a method that leverages large language models with retrieval-augmented generation to analyze tweets in relation to claims. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a new dataset . it shows that it outperformed existing methods and achieves a significant increase in Macro-F1 score on TSD-CT. |
Modeling News Interactions and Influence for Financial Market Prediction (2024.findings-emnlp)
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| Challenge: | Existing studies often adopt a simplified approach by treating available news data holistically and investigating its overall effect on market outcomes, the nuanced information contained within individual news items is overlooked. |
| Approach: | They propose a market prediction model that integrates multi-modal information from both market data and news articles to capture the links between news and prices. |
| Outcome: | The proposed model outperforms existing market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. |
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. |
| Approach: | They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations. |
| Outcome: | The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints. |
Towards Fine-grained Text Sentiment Transfer (P19-1)
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| Challenge: | Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm. |
| Approach: | They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content. |
| Outcome: | The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation. |
SentiArabic: A Sentiment Analyzer for Standard Arabic (L18-1)
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| Challenge: | Sentiment analysis is a process of applying computational approaches to identify attitudes, emotions and opinions in text, speech and visual data. |
| Approach: | They propose a sentiment analyzer that identifies the overall contextual polarity for Arabic text. |
| Outcome: | The proposed system achieves an F-score of 76.5% when evaluated on a blind test set. |
PoKi: A Large Dataset of Poems by Children (2020.lrec-1)
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| Challenge: | a new corpus of child-written texts is available for study of child language . authors use non-parametric regressions to model developmental differences from early childhood to late-adolescence . |
| Approach: | They propose to analyze 62 thousand child-written poems written by children from grades 1 to 12 . they use non-parametric regressions to model developmental differences from early childhood to late-adolescence . |
| Outcome: | The proposed corpus includes about 62 thousand poems written by children from grades 1 to 12 . results show decreases in valence that are especially pronounced during mid-adolescence . |
WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art (L18-1)
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| Challenge: | a dataset of 4,000 pieces of art has annotations for emotions evoked in the observer . the dataset can help answer questions about what makes art evocative, how does art convey different emotions, what attributes of a painting make it well liked, and how much does the title impact the affectual response to art. |
| Approach: | They create a dataset of 4,000 western art pieces that has annotations for emotions . they use crowdsourcing to annotate the art for one or more of twenty emotion categories . fear, happiness, love, sadness were the dominant emotions that obtained consistent annotations . |
| Outcome: | The dataset shows that the most popular emotions are fear, happiness, love and sadness . the dataset can be used to develop systems that detect emotions evoked by art . |
CEASE, a Corpus of Emotion Annotated Suicide notes in English (2020.lrec-1)
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| Challenge: | Existing studies on suicide notes have not explored the topic of emotion detection. |
| Approach: | They develop a fine-grained emotion annotated corpus of suicide notes in English and use it to perform emotion detection on a curated dataset. |
| Outcome: | The proposed model performs emotion detection on a curated dataset of 205 suicide notes in English. |
Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)
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| Challenge: | anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity. |
| Approach: | They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology. |
| Outcome: | The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors. |
A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions (2025.findings-naacl)
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| Challenge: | Existing literature on Arabic sentiment analysis is limited, compared to high-resourced languages such as English and French. |
| Approach: | They present a systematic review of existing literature on Arabic sentiment analysis focusing on research utilizing deep learning. |
| Outcome: | The proposed methods highlight gaps in the literature on Arabic sentiment analysis and outline promising directions for future research. |
Sentimental Image Generation for Aspect-based Sentiment Analysis (2025.findings-acl)
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| Challenge: | Recent work on textual Aspect-Based Sentiment Analysis (ABSA) has demonstrated promising performance, but limited semantics derived from raw data. |
| Approach: | They propose a method that provides visual semantics to reinforce textual ABSA by adding additional augmentations to the input data. |
| Outcome: | The proposed method can provide visual semantics to reinforce the textual extraction. |
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (D19-1)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose a framework to incorporate intent information into slot filling tasks . they use a joint model with Stack-Propagation to capture intent semantic knowledge . |
| Outcome: | The proposed model outperforms existing models on two publicly available datasets and outperformed existing models by a large margin. |
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis (2022.lrec-1)
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Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala, Shalaka Satheesh, Aswinkumar Vijayananth, Duc Bach Ha, Sven Behnke, Joachim Köhler
| Challenge: | Sentiment analysis and emotion recognition can help research in audiovisual interview archives . however, humans perceive sentiments and emotions ambiguously and subjectively . |
| Approach: | They investigate human perceptions of emotions and sentiments in oral history interviews . they show that human perception for different emotions is ambiguous and subjective . authors propose deep learning as a way to categorize and search emotions . |
| Outcome: | The proposed techniques can be used to search and index audiovisual interviews . the authors show that human perceptions differ for different emotions . |
Uncovering the Limits of Text-based Emotion Detection (2021.findings-emnlp)
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| Challenge: | Identifying emotions from text is crucial for a variety of downstream tasks. |
| Approach: | They consider the two largest now-available corpora for emotion classification: GoEmotions and Vent. |
| Outcome: | The proposed models outperform the two largest corpora for emotion classification: GoEmotions and Vent. |
Introducing a Lexicon of Verbal Polarity Shifters for English (L18-1)
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| Challenge: | Negation words can change the sentiment polarity of a phrase, but there are more than 1200 other polarities. |
| Approach: | They propose a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet. |
| Outcome: | The proposed lexicon covers the entirety of verbs found in WordNet. |
Distribution of Emotional Reactions to News Articles in Twitter (L18-1)
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| Challenge: | Social networks have created datasets of opinions of users that focus on the writers' perspective, which does not consider the source that provokes those opinions. |
| Approach: | They propose to analyze opinions of Twitter users' after reading a news article and use it to predict the distribution of emotions. |
| Outcome: | The proposed dataset aims to explore how the six emotions are expressed by Twitter users' after reading a news article. |
Modelling the interplay of metaphor and emotion through multitask learning (D19-1)
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| Challenge: | Existing research suggests that metaphorical phrases are more emotionally evocative than their literal counterparts. |
| Approach: | They propose a joint model of the relationship between metaphor and emotion within a computational framework by using hard and soft parameter sharing. |
| Outcome: | The proposed model advances the state of the art in both of these tasks. |
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment (P18-1)
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| Challenge: | SemAxis characterizes word semantics using many semantic axes in word-vector spaces beyond sentiment . lexicon-based text analysis assumes that meaning of words does not change across contexts . but, recent advances in vector-space representations can tackle this challenge . |
| Approach: | They propose a framework to characterize word semantics using many semantic axes beyond sentiment . they demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in building domain-specific sentiment lexicons. |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
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| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)
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| Challenge: | Existing studies only leverage dependency relations without considering their dependency types . a valid and effective approach is demonstrated on six English benchmark datasets . |
| Approach: | They propose to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks . attention is used in T-GCN to distinguish different edges in the graph and attentive layer ensemble to comprehensively learn from different layers of T-gCN. |
| Outcome: | The proposed approach performs well on six English benchmark datasets. |
Instruction-following Evaluation through Verbalizer Manipulation (2024.findings-naacl)
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| Challenge: | Existing benchmarks focus on common instructions that align well with what the model learned during training, but proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. |
| Approach: | They propose a new instruction-following evaluation protocol called verbalizer manipulation that instructs the model to verbalize the task label with words aligning with model priors to different extents. |
| Outcome: | The proposed protocol can be integrated with any classification benchmark to examine the model’s reliance on priors and its ability to override them to accurately follow the instructions. |
Exploring Graph Pre-training for Aspect-based Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies tend to extract the sentiment elements in a generative manner to avoid complex modeling of sentiment elements. |
| Approach: | They propose a generative model with an Element-level Graph Pre-training paradigm and a Task Decomposition Pre- training paradigm to make it generalizable and robust against irregular sentiment quadruples. |
| Outcome: | The proposed model is generalizable and robust against irregular sentiment quadruples. |
Aspect Based Sentiment Analysis with Gated Convolutional Networks (P18-1)
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| Challenge: | Aspect-based sentiment analysis can provide more detailed information than general sentiment analysis. |
| Approach: | They propose a model based on convolutional neural networks and gating mechanisms which can selectively output the sentiment features according to the given aspect or entity. |
| Outcome: | The proposed model can selectively output sentiment features according to the given aspect or entity. |
Predicting Discourse Structure using Distant Supervision from Sentiment (D19-1)
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| Challenge: | Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization. |
| Approach: | They propose a method that uses document supervision to generate abundant data for RST-style discourse structure prediction by using an optimal CKY-style tree generation algorithm. |
| Outcome: | The proposed approach performs well on the more difficult task of inter-domain discourse structure prediction, but it does not match the performance of a parser trained and tested on the same dataset. |
Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis (2025.findings-naacl)
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| Challenge: | Existing methods for inference require multiple sampling with preset size . however, it is a high-cost method that requires multiple sampling . |
| Approach: | They propose a method that combines multiple and single sampling to greatly reduce the cost of multiple sampling without sacrificing performance. |
| Outcome: | The proposed method greatly reduces the cost of multiple sampling without sacrificing performance. |
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs (C18-1)
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| Challenge: | Social media based micro-blogging sites like Twitter are used for expressing emotions and opinions. |
| Approach: | They propose to combine convolutional and fully connected layers in a non-sequential manner to train deep multi-task learning models trained for all emotions at once in unified architecture. |
| Outcome: | The proposed model outperforms the previous system by 0.044 or 4.4% on the WASSA’17 EmoInt shared task dataset. |
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing studies focus on improving the performance of domain-specific models based on the target dataset. |
| Approach: | They propose a Large Language Model-based Continual Learning (LLM-CL) model for ABSA that learns the target domain’s ability while maintaining the history domains’ abilities. |
| Outcome: | The proposed model obtains new state-of-the-art over 19 datasets. |
Adversarial Category Alignment Network for Cross-domain Sentiment Classification (N19-1)
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| Challenge: | Existing methods for cross-domain sentiment classification focus on aligning marginal distribution without taking category-specific decision boundaries into consideration. |
| Approach: | They propose an adversarial category alignment network to enhance category consistency . experimental results show the proposed method can achieve state-of-the-art performance . |
| Outcome: | The proposed method achieves state-of-the-art performance and produces more discriminative features on benchmark datasets. |
BeSt: The Belief and Sentiment Corpus (2022.lrec-1)
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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski
| Challenge: | a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards . |
| Approach: | They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish . |
| Outcome: | The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what. |
BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)
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Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response. |
| Approach: | They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses. |
| Outcome: | The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. |
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)
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| Challenge: | Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection. |
| Approach: | They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training. |
| Outcome: | The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods. |
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms (P19-1)
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| Challenge: | evaluating models is a non-trivial task and requires extensive data and data splits to produce reliable comparisons. |
| Approach: | They propose a model selection approach that reduces the computational resources required to compare models based on single choices of random seeds. |
| Outcome: | The proposed model selection approach reduces the computational resources required to identify state-of-the-art models from large datasets. |
CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms (2022.lrec-1)
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| Challenge: | Euphemisms are a difficult topic because they are subject to language change and humans may not agree on what is a euphemist. |
| Approach: | They analyze a corpus of potentially euphemistic terms (PETs) and examples from the GloWbE corpus to examine their meanings. |
| Outcome: | The proposed corpus of potentially euphemistic terms and examples from the GloWbE corpus show that PETs generally decrease negative and offensive sentiment. |
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis (2020.emnlp-main)
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| Challenge: | Existing methods to detect sentiment toward aspect categories ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance. |
| Approach: | They propose a multi-instance multi-label learning network for Aspect-Category sentiment analysis that treats sentences as bags, words as instances, and the words indicating an aspect category as key instances of the aspect category. |
| Outcome: | The proposed model is based on three public datasets showing that it performs well. |
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction (2020.acl-main)
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| Challenge: | Aspect terms and opinion terms are key problems of fine-grained aspect-based sentiment analysis. |
| Approach: | They propose a method to extract aspect and opinion terms as pairs from a sentence . they use shared spans to extract the terms under supervision of span boundaries . |
| Outcome: | The proposed method outperforms state-of-the-art methods on both aspects and opinion terms extraction tasks. |
Figurative Language in Recognizing Textual Entailment (2021.findings-acl)
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| Challenge: | Existing RTE models struggle to capture figurative language, despite its ubiquity, it remains a bottleneck in automatic text understanding. |
| Approach: | They propose to frame five existing figurative language datasets into over 12,500 RTE examples. |
| Outcome: | The proposed models struggle to perform pragmatic inference and reasoning about world knowledge. |
LLM-Driven Implicit Target Augmentation and Fine-Grained Contextual Modeling for Zero-Shot and Few-Shot Stance Detection (2025.emnlp-main)
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| Challenge: | Recent studies on zero-shot and few-shot stance detection neglect implicit yet semantically important targets. |
| Approach: | They propose a framework that uses Large Language Models to annotate implicit targets . they also propose 'DyMCA' to dynamically adjust text-target contributions based on context . |
| Outcome: | The proposed framework achieves state-of-the-art on a benchmark dataset. |
A Thesaurus-based Sentiment Lexicon for Danish: The Danish Sentiment Lexicon (2022.lrec-1)
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| Challenge: | a newly published Danish sentiment lexicon with a high lexical coverage was compiled using lexicographic methods and linked data. |
| Approach: | They propose to use lexicographic methods to compile a Danish sentiment lexicon with a high lexical coverage by linking words from a thesaurus to a comprehensive monolingual dictionary. |
| Outcome: | The proposed lexicon contains 13,859 Danish polarity lemmas and includes morphological information. |
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (2020.emnlp-main)
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| Challenge: | Existing methods for detecting public sentiment drift are not designed for sentiment drift detection. |
| Approach: | They propose a Hierarchical Variational Auto-Encoder model to learn better distribution representation and a new drift measure to directly evaluate distribution changes between historical and new data. |
| Outcome: | The proposed model performs better than three existing state-of-the-art methods. |
How Do LLMs Generate Contrastive Sentiments? A Mechanistic Perspective (2026.eacl-long)
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| Challenge: | Despite extensive research, the mechanisms underlying LLMs' abilities remain poorly understood. |
| Approach: | They propose and validate a mechanistic intervention that transforms the sentiment of a text from positive to negative while making minimal edits. |
| Outcome: | The proposed intervention increases sentiment flip rate without sacrificing minimal changes to text content. |
Why Is MBTI Personality Detection from Texts a Difficult Task? (2021.eacl-main)
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| Challenge: | Automatic detection of the four MBTI personality dimensions from texts has attracted noticeable attention from the natural language processing and computational linguistic communities. |
| Approach: | They propose to use a questionnaire-based personality assessment to provide more objective assessment of one's personality than traditional questionnaires. |
| Outcome: | The proposed systems rarely outperform the majority-class baseline despite large datasets and high levels of noise in training datasets. |
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework (2022.acl-long)
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| Challenge: | Existing datasets for emotion detection are heterogeneous in size, domain, format, splits, emotion categories and role labels, hampering progress in this area. |
| Approach: | They propose a framework for annotating emotions manually using a common labeling scheme to unify several datasets tagged with emotions and semantic roles. |
| Outcome: | The proposed framework unifies datasets tagged with emotions and semantic roles by using a common labeling scheme. |
Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks for multimodal satirical cognition hinder evaluation of multimodal Sarcasm Understanding . lack of a unified benchmark for holistic satire cognition hampers evaluation of MSU . |
| Approach: | They propose a framework to decouple experts into orthogonal shared perception and private execution streams to physically block gradient interference between tasks. |
| Outcome: | The proposed framework achieves superior performance on DocMSU-PLUS. |
Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)
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| Challenge: | Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets . |
| Approach: | They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results . |
| Outcome: | The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them. |
Identifying Sentiments in Algerian Code-switched User-generated Comments (2020.lrec-1)
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| Challenge: | a recent study has focused on sentiment analysis for the Arabic variety, but it has been extended to other domains. |
| Approach: | They build a corpus of 36,000 code-switched user-generated comments annotated for sentiments in Algerian Arabic. |
| Outcome: | The proposed model performs better on unedited code-switched and unbalanced data across sentiment classes. |
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis (2022.emnlp-main)
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Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, Nan Duan
| Challenge: | Existing pre-trained language representation models (PLMs) capture sentiment information from word-level while under-considering sentence-level information. |
| Approach: | They propose a Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks that enhance the PLM’s knowledge about sentiment words. |
| Outcome: | The proposed model achieves state-of-the-art on various sentence-level and aspect-level sentiment classification benchmarks. |
Beyond Understanding: Evaluating the Pragmatic Gap in LLMs’ Cultural Processing of Figurative Language (2026.eacl-long)
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| Challenge: | Using figurative language as a proxy for cultural nuance and local knowledge, large language models struggle with connotative meaning. |
| Approach: | They evaluate large language models' ability to process culturally grounded language . they use figurative language as a proxy for cultural nuance and local knowledge . |
| Outcome: | The proposed models can understand and use figurative expressions that encode local knowledge and social nuance. |
Non-Compositionality in Sentiment: New Data and Analyses (2023.findings-emnlp)
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| Challenge: | Many studies on sentiment analysis focus on the fact that sentiment computations are compositional . linguistic utterances often do not adhere to strict patterns and can be surprising when looking at the individual words involved. |
| Approach: | They propose a method for obtaining non-compositionality ratings for phrases with respect to their sentiment . they also propose evaluating computational models for sentiment analysis using the rating resource . |
| Outcome: | The proposed method enables non-compositional ratings for phrases with respect to their sentiment . the results are compared with a new resource of ratings for 259 phrases . |
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)
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| Challenge: | Social media are integrated with our daily life and are used to circulate information. |
| Approach: | They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign. |
| Outcome: | The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region. |
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection (2021.emnlp-main)
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| Challenge: | Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately. |
| Approach: | They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis. |
| Outcome: | The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects. |
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue. |
| Approach: | They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence. |
| Outcome: | The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios. |
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin. (2020.lrec-1)
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| Challenge: | a new paper aims to provide sentiment analysis tools for ancient languages . the current sentiment analysis resources only cover modern languages based on textual typologies . |
| Approach: | They propose to use manually-curated Latin lexicons to evaluate sentiment analysis tools . they propose a gold standard and a silver standard for evaluating lexical items . |
| Outcome: | The proposed lexicons are evaluated using a gold standard and a silver standard for sentiment analysis. |
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis (D18-1)
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Deepanway Ghosal, Md Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing methods for multi-modal sentiment analysis are limited due to the use of text, visual and acoustic inputs. |
| Approach: | They propose a recurrent neural network based multi-modal attention framework that leverages contextual information for utterance-level sentiment prediction. |
| Outcome: | The proposed framework performs better on two multi-modal sentiment analysis benchmark datasets with accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets. |
Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks (2024.lrec-main)
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| Challenge: | 3.1K reviews are manually annotated for aspect-based sentiment analysis (ABSA) ABSA is a fine-grained task that aims to identify the sentiment associated with each aspect or characteristic of a text. |
| Approach: | They propose a new Czech dataset for aspect-based sentiment analysis . the new dataset is built upon the older Czech dataset . authors provide 24M reviews without annotations suitable for unsupervised learning . |
| Outcome: | The proposed dataset is built upon the older dataset, but is specifically designed for more complex tasks. |
Aspect-based Sentiment Analysis in Question Answering Forums (2021.findings-emnlp)
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| Challenge: | Existing studies on aspects-based sentiment analysis focus on a single opinionated sentence. |
| Approach: | They propose a model to combine aspects and their sentiments for QA forums . they use cross-sentence aspect-opinion interaction modeling to align the aspect mentioned in the question and associated opinion clues in the answer. |
| Outcome: | The proposed model outperforms baseline models on three real-world datasets. |
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)
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| Challenge: | Emotion and sentiment classification in dialogues has gained popularity in recent times . a number of datasets are imbalanced in representing different emotions and consist of an only single emotion. |
| Approach: | They propose to use a dataset to analyze emotions and sentiments in dialogues . they use text, audio and video to identify the correct emotions with the appropriate intensity and sentiment in an utterance of a dialogue . |
| Outcome: | The proposed datasets are balanced in representing different emotions and consist of only one emotion. |
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance (2024.findings-acl)
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| Challenge: | Existing studies have focused on developing ad hoc models of varying complexity for ABSA subtasks. |
| Approach: | They propose an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on most domains and achieves competitive results on biomedical domain datasets. |
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
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| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
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| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
| Approach: | They propose a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision. |
| Outcome: | The proposed framework improves over baselines and shows that opinion summaries are preferred by human judges according to multiple criteria. |
DEEM: Dynamic Experienced Expert Modeling for Stance Detection (2024.lrec-main)
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| Challenge: | Existing work on stance detection tasks using large language models shows promising results, but it may not be able to provide detailed background knowledge. |
| Approach: | They propose a method which leverages the generated experienced experts and lets LLMs reason in a semi-parametric way. |
| Outcome: | The proposed method outperforms methods with self-consistency reasoning and reduces bias. |
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis (2022.emnlp-main)
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| Challenge: | Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. |
| Approach: | They propose a weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. |
| Outcome: | The proposed framework outperforms weakly supervised baselines on four benchmark datasets and is able to generate multiple aspect category-sentiment pairs per review sentence. |
Complex and Precise Movie and Book Annotations in French Language for Aspect Based Sentiment Analysis (L18-1)
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| Challenge: | Aspect Based Sentiment Analysis (ABSA) aims at collecting detailed opinion information according to products and their features. |
| Approach: | They propose to use linguistics tools to enhance text classification with aspect-based sentiment analysis. |
| Outcome: | The proposed method is based on two French online reviews datasets. |
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis (L18-1)
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| Challenge: | Sentiment Analysis is a subtask of Natural Language Processing. |
| Approach: | They propose a new, domain-neutral lexicon for sentiment analysis in English . they test it on two publicly available sentiment analysis datasets . |
| Outcome: | The proposed lexicon performs better than existing sentiment lexiconics on two publicly available datasets. |
Cross-Domain Review Generation for Aspect-Based Sentiment Analysis (2021.findings-acl)
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| Challenge: | Existing domain adaptation methods for Aspect-Based Sentiment Analysis lack finegrained labeled data. |
| Approach: | They propose a new domain adaptation paradigm called cross-domain review generation which aims to generate target-domain reviews with fine-grained annotation based on the labeled source domain. |
| Outcome: | The proposed approach is superior to state-of-the-art domain adaptation methods. |
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection (2021.naacl-main)
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| Challenge: | Existing datasets for sarcasm detection are limited due to the difficulty in acquiring ground-truth annotations. |
| Approach: | They propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics. |
| Outcome: | The proposed approach outperforms transfer learning and meta-learning baselines and achieves 10.02% performance gain on the iSarcasm dataset. |
Generating a Gold Standard for a Swedish Sentiment Lexicon (L18-1)
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| Challenge: | Existing sentiment lexicons are compiled by (machine) translation from English resources, obscuring language-specific characteristics of sentiment-loaded vocabulary. |
| Approach: | They propose a gold standard for sentiment annotation of Swedish terms using the SALDO lexicon and the Gigaword corpus. |
| Outcome: | The proposed model is based on the free SALDO lexicon and the Gigaword corpus and is compared with existing models using human annotations. |
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate (2022.naacl-main)
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Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser
| Challenge: | Covid-19 infodemic has led to low quality information leading to poor health decisions . authors propose a framework for analyzing false claims and reasoning about the decisions a person makes . |
| Approach: | They propose a framework linking stance and reason analysis and moral sentiment analysis. |
| Outcome: | The proposed framework provides reliable predictions even in low-supervision settings. |
Dynamic Stance: Modeling Discussions by Labeling the Interactions (2023.findings-emnlp)
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| Challenge: | Stance detection is a popular task that has been modeled as a static task, but its limitations are strong topic-dependent. |
| Approach: | They propose to model stance as a dynamic task by focusing on interactions between a message and their replies. |
| Outcome: | The proposed model shows portability across topics and languages. |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms . |
| Approach: | They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table. |
| Outcome: | The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task. |
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)
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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
| Challenge: | Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages. |
| Approach: | They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets. |
| Outcome: | The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. |
Few-shot Pseudo-Labeling for Intent Detection (2020.coling-main)
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| Challenge: | Existing methods for labeling intents are expensive and time-consuming. |
| Approach: | They propose a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. |
| Outcome: | The proposed method performs better on multiple intent detection datasets and is stronger than existing methods. |
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English (2025.findings-acl)
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| Challenge: | despite large language models showing bias against non-mainstream varieties, there are no labeled datasets for sentiment analysis of English. |
| Approach: | They propose a benchmark for sentiment and sarcasm classification for three varieties of English . they manually annotate the datasets with sentiment and the sarcasmatic labels . |
| Outcome: | The proposed benchmark is based on a web-based content from Google Place reviews and Reddit comments. |
Joint Intent Detection and Entity Linking on Spatial Domain Queries (2020.findings-emnlp)
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| Challenge: | Spatial domain queries have unique properties making them more challenging for language understanding than common conversational queries. |
| Approach: | They propose a language understanding framework for spatial domain queries that jointly learns the intent detection and entity linking tasks on a voice assistant service. |
| Outcome: | The proposed framework outperforms baseline methods with a significant margin. |
Utilizing Large Twitter Corpora to Create Sentiment Lexica (L18-1)
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| Challenge: | Existing sentiment analysis systems only use word unigrams and bigrams, but lexicons using sentiment lexica are effective. |
| Approach: | They describe an automatic Twitter sentiment lexicon creator and a lexico-based sentiment analysis system. |
| Outcome: | The proposed system outperforms a manually annotated system in a comparison experiment. |
Human Rationales as Attribution Priors for Explainable Stance Detection (2021.emnlp-main)
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| Challenge: | In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Approach: | They propose a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data. |
| Outcome: | The proposed method improves the reasoning of a state-of-the-art classifier in a data-scarce setting at no cost in predictive performance. |
Inducing Target-Specific Latent Structures for Aspect Sentiment Classification (2020.emnlp-main)
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| Challenge: | Aspect-level sentiment analysis aims to classify the sentiment polarity of an aspect or a target in a comment . graph convolutional networks can be used to classifice aspect terms in syllables . |
| Approach: | They propose to combine word dependency graphs and latent graphs to create latent models . they propose to model the interaction between the aspect and its surrounding contexts . |
| Outcome: | The proposed model can complement syntactic features with latent semantic dependencies. |
Deep Weighted MaxSAT for Aspect-based Opinion Extraction (2020.emnlp-main)
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| Challenge: | Existing deep learning models lack the capability to encode explicit domain knowledge to model complex causal relationships among variables. |
| Approach: | They propose a model that uses a weighted version of MaxSAT to model logic inference . they propose to use this model to rectify erroneous predictions from deep neural networks . |
| Outcome: | The proposed model combines the benefits of high-level feature learning, knowledge reasoning, and structured learning with observable performance gain for aspect-based opinion extraction. |
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (P19-1)
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Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria
| Challenge: | sarcasm is often expressed through multiple verbal and non-verbal cues, such as a change of tone, overemphasis, drawn-out syllables, or a straight looking face. |
| Approach: | They propose to use multimodal cues to improve sarcasm detection using audiovisual utterances annotated with sarcasm labels to improve the accuracy. |
| Outcome: | The proposed dataset reduces the error rate of sarcasm detection by 12.9% . it is based on audiovisual utterances annotated with sarcasm labels . |
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (D19-1)
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| Challenge: | Existing methods for aspect-specific sentiment classification are noisy and downgraded performance. |
| Approach: | They propose a constrained attention network to regularize attention for multi-aspect sentiment analysis by orthogonal regularization on multiple aspects and sparse regularization for each single aspect. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets and extends to multi-task settings. |
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to domain adaptation fail to generalize well on unknown test data. |
| Approach: | They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
| Outcome: | The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation (2025.findings-naacl)
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| Challenge: | Existing systems that generate sarcastic explanations neglect the significance of the intended irony in generating them. |
| Approach: | They propose a multimodal sarcasm explanation that leverages the inter-modality relationships between an image and its caption to learn the intended irony for explanations. |
| Outcome: | The proposed model outperforms existing models on the sarcastic post-processing task and shows that it is more accurate than existing models. |
Text-based inference of moral sentiment change (D19-1)
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| Challenge: | Existing work in NLP treats moral sentiment as a flat classification problem, but our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and finegrained categories informed by Moral Foundations Theory. |
| Approach: | They propose a text-based framework that exploits implicit moral biases learned from diachronic word embeddings to probe moral sentiment change over a long historical period. |
| Outcome: | The proposed framework supports inferences of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions. |
Seeds of Discourse: A Multilingual Corpus of Direct Quotations from African Media on Agricultural Biotechnologies (2025.findings-naacl)
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| Challenge: | a new study examines how media amplify messages around GM crops . agribusiness companies have placed ads for their products in newspapers . |
| Approach: | They present a multilingual corpora of 1,657 direct quotes from Africa-based news sources . they provide 665 instances annotated for Aspect-Based Sentiment Analysis . |
| Outcome: | The results of this study are available in English and French. |
Measuring Forecasting Skill from Text (2020.acl-main)
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| Challenge: | Prior studies have shown that some individuals can make accurate predictions with consistently better accuracy. |
| Approach: | They examine linguistic factors associated with people's predictions including uncertainty, readability, and emotion. |
| Outcome: | The proposed model can accurately predict forecasting skill using only language. |
FigMemes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes (2022.emnlp-main)
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| Challenge: | FigMemes is a dataset for figurative language classification in politically-opinionated memes. |
| Approach: | They propose to use figurative language classification to identify politically-opinionated memes by analyzing their datasets and comparing them to other machine learning models. |
| Outcome: | The proposed dataset includes annotations of six commonly used types of figurative language in politically-opinionated memes and a wide range of topics and visual styles. |
Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews. |
| Approach: | They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets and validates it. |
Joint Learning for Targeted Sentiment Analysis (D18-1)
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| Challenge: | Recent studies have attempted to perform two tasks separately, e.g., target extraction and sentiment classification. |
| Approach: | They propose a hierarchical stack bidirectional gated recurrent units (HSBi-GRU) model which allows the target label to influence their sentiment label. |
| Outcome: | The proposed model outperforms baseline models on two datasets and shows that it can learn abstract features. |
Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments. |
| Approach: | They propose an opinion tree parsing model which parses all sentiment elements from an opinion-tree. |
| Outcome: | The proposed model is much faster than previous models and can explore correlations among sentiment elements. |
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2024.lrec-main)
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| Challenge: | Emotion analysis (EA) is a rapidly growing field in natural language processing . there is no consensus on scope, direction, or methods for EA . |
| Approach: | They review 154 relevant NLP papers on emotion analysis from the last decade . they ask: how are EA tasks defined in NLP? what are the most prominent emotion frameworks and which emotions are modeled? |
| Outcome: | The authors examine 154 relevant NLP papers on emotion analysis from the last decade . they find that there is no consensus on scope, direction, or methods . |
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness (D18-1)
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| Challenge: | Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. |
| Approach: | They propose a co-attention neural network model for emotion cause analysis with emotional context awareness. |
| Outcome: | The proposed model outperforms the state-of-the-art methods. |
Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity (2020.acl-main)
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| Challenge: | Existing methods for predicting a post's stance polarity and intensity don't take into account the stance's degree of intensity. |
| Approach: | They propose to use a new problem to train models for predicting a post's stance polarity and intensity in cyber argumentation. |
| Outcome: | The proposed models perform best for prediction of stance polarity with an accuracy of 70.43% and intensity with RMSE of 0.596. |
Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues (2025.findings-acl)
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David Sasu, Zehui Wu, Ziwei Gong, Run Chen, Pengyuan Shi, Lin Ai, Julia Hirschberg, Natalie Schluter
| Challenge: | Akan Cinematic Emotions (AkaCE) is the first multimodal emotion dialogue dataset for an African language . it contains 385 emotion-labeled dialogues and 6162 utterances across audio, visual, and textual modalities, along with word-level prosodic prominence annotations. |
| Approach: | They propose to use AkaCE to analyze African cinematic emotions using word-level prosodic prominence annotations. |
| Outcome: | The Akan Cinematic Emotions (AkaCE) dataset addresses the significant lack of resources for low-resource languages in emotion recognition research. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
A Sequence-to-Structure Approach to Document-level Targeted Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has received wide attention in NLP for nearly two decades . previous studies focused on sentence-level ABSA, but document-level research has not received enough attention. |
| Approach: | They propose a Sequence-to-Structure approach to address the document-level targeted sentiment analysis task, which aims to extract the opinion targets consisting of multi-level entities from a review document and predict their sentiments. |
| Outcome: | The proposed approach outperforms baselines on six domains on the document-level targeted sentiment analysis task. |
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information. |
| Approach: | They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects. |
| Outcome: | The proposed method is superior to existing methods in the field of sentiment analysis. |
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)
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| Challenge: | Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content. |
| Approach: | They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well. |
Multi-lingual and Multi-cultural Figurative Language Understanding (2023.findings-acl)
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Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji, Genta Winata, Samuel Cahyawijaya, Anuoluwapo Aremu, Perez Ogayo, Graham Neubig
| Challenge: | Figures permeate human communication, but are understudied in NLP. |
| Approach: | They create a figurative language inference dataset for seven languages associated with a variety of cultures, using cultural and regional concepts for figurativ expressions. |
| Outcome: | The results show that the most common figurative expressions are found in Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. |
ELAL: An Emotion Lexicon for the Analysis of Alsatian Theatre Plays (2022.lrec-1)
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| Challenge: | a novel and manually corrected emotion lexicon is presented for Alsatian dialects . the dialects are used mainly orally and lack a stable and consensual spelling convention . |
| Approach: | They propose a novel and manually corrected emotion lexicon for Alsatian dialects . they use graphical variants of Alsalian lexical items to perform automatic emotion analysis . |
| Outcome: | The novel and manually corrected emotion lexicon is used to perform automatic emotion analysis in Alsatian theatre plays. |
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
Deep Unknown Intent Detection with Margin Loss (P19-1)
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| Challenge: | Existing methods for detecting unknown intents are difficult due to lack of examples. |
| Approach: | They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor. |
| Outcome: | The proposed method can yield consistent improvements on two benchmark datasets. |
Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments (2024.lrec-main)
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| Challenge: | sarcasm detection remains an issue for both humans and natural language processing models . |
| Approach: | They analysed 300 comments from the FigLang 2020 Reddit Dataset and 39 non-native speakers of English to see if they were sarcastic. |
| Outcome: | The results show that the models and models have similar performance and weaknesses when the comments include political topics or are phrased as questions. |
Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery (D19-1)
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| Challenge: | Existing approaches to inference opinionated text do not capture attributes in a one-off manner. |
| Approach: | They propose a probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. |
| Outcome: | The proposed model outperforms state-of-the-art models and yields intuitive topics. |
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)
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| Challenge: | Existing models use aspect-independent encoders for sentence representation generation. |
| Approach: | They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture. |
| Outcome: | The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features. |
Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction (2023.acl-long)
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| Challenge: | sarcasm is a form of irony conveying mockery and contempt . social media has become increasingly popular for identifying sarcasm . |
| Approach: | They develop a method to detect sarcasm from social media using augmented potentials. |
| Outcome: | The proposed method outperforms baselines on benchmark datasets. |
Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for sarcasm detection are limited by supervised learning or prompt engineering . a new approach decomposes sarcasm detection into three dimensions: language, context, and emotion . |
| Approach: | They propose a method that decomposes sarcasm detection into three dimensions: language, context, and emotion. |
| Outcome: | The proposed method outperforms state-of-the-art methods in most cases. |
Decode with Template: Content Preserving Sentiment Transfer (2020.lrec-1)
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| Challenge: | Existing methods to transfer sentiments for text use only explicit sentiments and templates to remove them from input sentences. |
| Approach: | They propose a method to transfer sentiments from input sentences to output sentences using templates. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models in content preservation. |
Perturbation Sensitivity Analysis to Detect Unintended Model Biases (D19-1)
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| Challenge: | Recent research shows that data-driven NLP models may inadvertently capture, reflect and sometimes amplify various social biases present in the language data they are trained on. |
| Approach: | They propose a generic evaluation framework that detects unintended model biases related to named entities and requires no new annotations or corpora. |
| Outcome: | The proposed framework detects unintended model biases related to named entities and requires no new annotations or corpora. |
From Heard to Lived Opinions: Simulating Opinion Dynamics with Grounded LLM Agents in Economic Environments (2026.findings-acl)
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| Challenge: | Existing studies on opinion dynamics (OD) focus primarily on opinion exchange, with opinion change driven by linguistic interaction. |
| Approach: | They propose a OD simulation framework that grounds LLM-based agents in an economic environment and allows them to act and receive environmental feedback. |
| Outcome: | The proposed framework shows that LLM-based agents can act and receive environmental feedback at both individual and population levels while generating larger distributional shifts. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
Improving Document-Level Sentiment Analysis with User and Product Context (2020.coling-main)
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| Challenge: | Existing work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. |
| Approach: | They propose to incorporate all available historical review text belonging to the author of the review in question and investigate the inclusion of his- torical reviews associated with the current product. |
| Outcome: | The proposed model improves on IMDB, Yelp 2013 and Yelpan 2014 datasets by more than 2 percentage points in the best case. |
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)
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| Challenge: | Sentiment analysis in social media is challenging because of the lack of context. |
| Approach: | They propose to use stickers to perform a multimodal sentiment analysis task using Chinese stickers. |
| Outcome: | The proposed model performs best compared with other models. |
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods for Aspect-based sentiment analysis (ABSA) focus on aspect terms with the same sentiment polarity . current methods focus on sentences with only one aspect term or multiple aspect terms . |
| Approach: | They propose a novel method to model inter-aspect relationships and aspect-context relationships simultaneously using a heterogeneous graph. |
| Outcome: | The proposed method can predict sentiments towards the given aspect term in a sentence . it can provide more detailed predictions compared with sentence-level sentiment analysis. |
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance (2022.coling-1)
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| Challenge: | Existing methods to learn complex sentence with multiple aspects do not consider correlation between aspects to distinguish overlapped feature. |
| Approach: | They propose a method that uses aspect correlation to improve aspect correlation modeling . they use Recurrent Mechanism to improve the joint representation of aspects . |
| Outcome: | The proposed method is state-of-the-art in multiaspect scenarios. |
Rhetorical Device-Aware Sarcasm Detection with Counterfactual Data Augmentation (2025.findings-acl)
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| Challenge: | Sarcasm is a complex form of sentiment expression widely used in human daily life. |
| Approach: | They propose a device-aware sarcasm dataset with counterfactually augmented data to capture its complexity. |
| Outcome: | The proposed dataset shows that it is more balanced than zero-shot models. |
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification (2020.lrec-1)
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| Challenge: | Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect Based Sentimence Analysis (ABSA) . recent deep transfer-learning methods have been applied successfully to a myriad of NLP tasks. |
| Approach: | They propose to use a self-supervised domain-specific BERT language model to exploit ATSC . they also perform cross-domain evaluation to explore the real-world robustness of their models . |
| Outcome: | The proposed model outperforms baseline models on the SemEval 2014 task 4 restaurants dataset. |
Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness. |
| Approach: | They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment. |
| Outcome: | The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable. |
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025.coling-main)
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| Challenge: | Existing methods for sarcasm detection lack commonsense inferential ability when faced with complex situations. |
| Approach: | They propose a commonsense reasoning framework for sarcasm detection based on commonsensense augmentation to supplement commonsence knowledge and infer the incongruity. |
| Outcome: | The proposed framework is able to detect sarcasm in five datasets and is robust to complex scenarios. |
Exploring the Emotional Dimension of French Online Toxic Content (2024.lrec-main)
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| Challenge: | Emotion annotations can be used to analyze content and can be applied to content analysis. |
| Approach: | They propose to use a corpus annotation scheme to annotate three online data sets composed of extremist, sexist and hateful messages respectively. |
| Outcome: | The proposed method can provide new insights for content analysis and stronger empirical background for automatic content detection. |
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing generative methods focus on a single task at a time. |
| Approach: | They propose a unified generative multi-task framework that can solve multiple ABSA tasks . they propose to control the type of task prompts consisting of multiple element prompts . |
| Outcome: | The proposed framework achieves state-of-the-art results in almost all ABSA tasks and competitive results in task transfer scenarios. |
An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing approaches to Aspect-Based Sentiment Analysis (ABSA) are lacking in a comprehensive evaluation and fair comparison. |
| Approach: | They propose to use a knowledge-mining method to build a large-scale knowledge-annotated SPT corpus and integrate sentiment knowledge into pre-training. |
| Outcome: | The proposed method is able to build a large-scale knowledge-annotated SPT corpus and compares with other methods. |
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language (2020.lrec-1)
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| Challenge: | Aspect Based Sentiment Analysis (ABSA) is a finer level sentiment analysis that assigns polarity to each targeted aspect instead of the entire review. |
| Approach: | They propose to use Telugu as a language for aspect based sentiment analysis . they use a resource that can be used to classify and categorise aspects of a review . |
| Outcome: | The proposed resource is based on a set of tasks in Telugu which demonstrate its reliability and usefulness. |
A Fine-grained Sentiment Dataset for Norwegian (2020.lrec-1)
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| Challenge: | Using a dataset for fine-grained sentiment analysis in Norwegian, we examine the annotation effort and provide an overview of the developed annotation guidelines. |
| Approach: | They propose a dataset for fine-grained sentiment analysis in Norwegian . they provide an overview of the developed annotation guidelines and analyze inter-annotator agreement . |
| Outcome: | The proposed dataset is the first of its kind for Norwegian and is available online. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
| Outcome: | The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts. |
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection (2022.coling-1)
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| Challenge: | Existing stance detection models use sentiment and commonsense knowledge to classify stance toward documents and topics . obtaining rich annotated data in stance detector is time-consuming and laborintensive . |
| Approach: | They propose to use sentiment and commonsense knowledge to boost transferability of stance detection model by using sentiment and similar knowledge. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark datasets. |
Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News (2020.lrec-1)
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| Challenge: | SentiEcon is a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications. |
| Approach: | They describe a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications. |
| Outcome: | The proposed lexicon significantly improves when adding sentiment words to the general-language sentiment lexiconic. |
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)
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| Challenge: | Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity. |
| Approach: | They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features. |
| Outcome: | The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features. |
Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)
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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
| Challenge: | Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes. |
| Approach: | They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender. |
| Outcome: | The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning. |
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025.coling-main)
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| Challenge: | Existing generative ASQP approaches do not model the contextual relationship of the review sentence to predict implicit terms. |
| Approach: | They propose an extractive ASQP framework, CACA, which features with Context-Aware Cross-Attention Network to enhance alignment of aspects and opinions. |
| Outcome: | The proposed framework improves the alignment of aspects and opinions, whether explicit or implicit, and improves on three benchmark datasets. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis (D19-1)
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| Challenge: | Existing ABSA methods only use one aspect or multiple aspects with the same sentiment polarity . recent studies show that neural network methods can be trained end-to-end and automatically learn important features. |
| Approach: | They propose a large-scale multi-aspect multi-sentiment dataset with two different aspects with different sentiment polarities. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the large-scale dataset . it is based on a novel neural network approach that can be trained end-to-end . |
Multi-Task Stance Detection with Sentiment and Stance Lexicons (D19-1)
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| Challenge: | Recent studies show improvements in stance detection by using attention mechanism or sentiment information. |
| Approach: | They propose a multi-task framework that incorporates attention mechanism and takes sentiment classification as an auxiliary task. |
| Outcome: | The proposed model outperforms state-of-the-art deep learning methods on the SemEval-2016 dataset. |
Scalable Visualisation of Sentiment and Stance (L18-1)
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| Challenge: | a novel visualisation approach for sentiment and stance analysis is proposed for large datasets. |
| Approach: | They propose a visualisation approach for scalable visualisation of sentiment and stance from large-scale data. |
| Outcome: | The proposed visualisation approach can be used on a 9,278 user comments with stance explicitly declared by the author. |
NoReC: The Norwegian Review Corpus (L18-1)
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Erik Velldal, Lilja Øvrelid, Eivind Alexander Bergem, Cathrine Stadsnes, Samia Touileb, Fredrik Jørgensen
| Challenge: | The Norwegian Review Corpus is a dataset of full-text reviews from major news sources. |
| Approach: | This paper presents the Norwegian Review Corpus, created for document-level sentiment analysis. |
| Outcome: | The corpus comprises more than 35,000 full-text reviews from a range of different domains. |
SenSALDO: Creating a Sentiment Lexicon for Swedish (L18-1)
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| Challenge: | sentiment analysis has seen an explosive expansion over the last decade or so . many theoretical and methodological questions remain unanswered and resource gaps unfilled . |
| Approach: | They develop a sentiment lexicon for written (standard) Swedish using an existing dataset . they assign a real value sentiment score in the range [-1,1] and produce a label for it . |
| Outcome: | The proposed sentiment lexicon is an open source resource from the Swedish Language Bank . it is based on an existing gold standard dataset and is available from Sprkbanken . |
Corpus Building and Evaluation of Aspect-based Opinion Summaries from Tweets in Spanish (L18-1)
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Daniel Peñaloza, Rodrigo López, Juanjosé Tenorio, Héctor Gómez, Arturo Oncevay-Marcos, Marco A. Sobrevilla Cabezudo
| Challenge: | a corpus of Spanish extractive and abstractive summaries of opinions is presented . the goal is to analyze the summary content and to show how different they are written . |
| Approach: | They present a corpus of Spanish extractive and abstractive summaries of opinions . they analyze the summary agreement between them and their aspect coverage and sentiment orientation . |
| Outcome: | The presented corpus of Spanish extractive and abstractive summaries is a reference for academic research. |
Incorporating Label Dependencies in Multilabel Stance Detection (D19-1)
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| Challenge: | Stance detection is a well-studied task in social media, but previous work focused on multiclass versions of the problem where labels are mutually exclusive. |
| Approach: | They propose a method that explicitly incorporates label dependencies in the training objective and reduces multilabel to multiclass learning. |
| Outcome: | The proposed method improves on two out of three datasets and reduces multilabel to multiclass learning. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |
Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)
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| Challenge: | Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated. |
| Approach: | They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models. |
| Outcome: | The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform. |
Human and System Perspectives on the Expression of Irony: An Analysis of Likelihood Labels and Rationales (2024.lrec-main)
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| Challenge: | a new study examines the recognition of irony by humans and automatic systems . a fine-grained annotation scheme allows for improved modeling of ironity in automatic systems. |
| Approach: | They propose a fine-grained annotation scheme that allows for better recognition of irony by humans and automatic systems. |
| Outcome: | The proposed model improves on tweets annotated with high confidence and agreement . it also performs better on high-confidence and highagreement samples compared to automated systems . |
Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)
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| Challenge: | In the NLP community, many researchers have begun to use machine learning on financial and economic data. |
| Approach: | They present a dataset with 10,000 financial tweets annotated by experts from the front desk and the middle desk in a bank’s treasury. |
| Outcome: | The annotated financial tweets of a bank's front desk and middle desk are compared against a general sentiment dictionary and a domain-specific dictionary. |
A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language . there is no standard Japanese dataset available for ABSA task in the language - a paper by cnn. |
| Approach: | They propose to use a Japanese aspect-based sentiment analysis dataset for hotel reviews domain . they propose to include 53,192 review sentences with seven aspect categories and two polarity labels . |
| Outcome: | The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. |
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain (2022.lrec-1)
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Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
| Challenge: | Existing studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. |
| Approach: | They extend the WRIME dataset with basic emotion intensity from both the writer's subjective and reader's perspective to include the Japanese sentiment polarity. |
| Outcome: | The proposed dataset is the first large-scale corpus to annotate both basic emotions and sentiment polarity labels from both the writer’s and reader’s perspectives. |
Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a major research topic in NLP since social networking services have increased . but the recognition of implicit sentiments that do not contain obvious opinion words remains unexplored . elcom captures document-level coherence by using contrastive learning and sentence-level by a hypergraph . |
| Approach: | They propose aspect-category enhanced learning with a neural coherence model . it captures document-level coherency by contrastive learning and sentence-level by a hypergraph . |
| Outcome: | The proposed model captures document-level coherence by using contrastive learning and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. |
BTC-SAM: Leveraging LLMs for Generation of Bias Test Cases for Sentiment Analysis Models (2025.emnlp-main)
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Zsolt T. Kardkovács, Lynda Djennane, Anna Field, Boualem Benatallah, Yacine Gaci, Fabio Casati, Walid Gaaloul
| Challenge: | Sentiment Analysis (SA) models harbor inherent social biases that can be harmful in real-world applications. |
| Approach: | They propose a bias testing framework that generates high-quality test cases using Large Language Models (LLMs) for the controllable generation of test sentences. |
| Outcome: | The proposed framework generates high-quality test cases for bias testing in SA models with minimal specification using Large Language Models (LLMs) for the controllable generation of test sentences. |
Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)
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| Challenge: | Existing systems for sentiment analysis are focused on document and sentence levels, but there are no public datasets on aspect-based sentiment analysis for Farsi. |
| Approach: | They propose to use a manually annotated Farsi dataset to analyze the opinion polarity of reviews . they also use transfer learning to analyze aspects of the review to improve their results . |
| Outcome: | The proposed method performs better than other aspects of the existing system. |
Sentiment Analysis of Homeric Text: The 1st Book of Iliad (2022.lrec-1)
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| Challenge: | Sentiment analysis studies focus more on online customer reviews and social media texts, but are less on literary studies. |
| Approach: | They propose to model the perceived sentiment of Iliad verses using a deep learning masked language model and a pre-trained model to estimate the sentiment of the poem. |
| Outcome: | The proposed model shows that sentiment estimators can be used as mechanical annotators, thus facilitating the distant reading of Homeric text. |
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter (2020.lrec-1)
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| Challenge: | Current scholarship is yet to reach an agreement on a universal definition of the concept of irony. |
| Approach: | They propose to query Twitter using irony-related hashtags to collect ironic messages which are then manually annotated by two linguists according to their working definition of irony. |
| Outcome: | The proposed corpus will be a valuable resource for developing open domain systems for automatic irony recognition in Arabic and its dialects in social media text. |
A Unified One-Step Solution for Aspect Sentiment Quad Prediction (2023.findings-acl)
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| Challenge: | Existing ASQP datasets are small and low-density, hindering technical advancement . et al. (2017): aspect sentiment quad prediction provides a complete aspect-level sentiment structure. |
| Approach: | They propose a one-step solution for Aspect sentiment quad prediction that can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
| Outcome: | The proposed solution can detect aspect categories and identify aspectopinion-sentiment triplets simultaneously. |
Learning Trajectories of Figurative Language for Pre-Trained Language Models (2025.findings-emnlp)
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| Challenge: | Figures of speech and figures of language are used in everyday communication . however, this imaginative use of words requires a solid understanding of semantics and real-world knowledge. |
| Approach: | They exploit probing tasks to analyse how NLMs recognise figurative language . they find out which layers have a better comprehension of figurativ language based on pre-training data. |
| Outcome: | The proposed model can recognise hyperboles, metaphors, oxymorons and pleonasms . data show which layers have a better comprehension of figurative language . |
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference (2023.findings-emnlp)
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| Challenge: | Existing clickbait detection models rely on analyzing the objective semantics of posts or correlating posts with article content only, but fail to identify and exploit the manipulation intention of clickbaiting from a user’s subjective perspective. |
| Approach: | They propose a multiview clickbait detection model to model subjective and objective preferences simultaneously to capture clickbaiting from a user's subjective perspective. |
| Outcome: | The proposed model outperforms state-of-the-art models on two real-world datasets and shows that it integrates subjective and objective preferences simultaneously. |
ZeroStance: Leveraging ChatGPT for Open-Domain Stance Detection via Dataset Generation (2024.findings-acl)
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
| Approach: | They propose a method for stance detection that trains a model that can generalize well to unseen targets across multiple domains. |
| Outcome: | The proposed method generalizes well to unseen targets across multiple domains over baselines on most benchmarks. |
Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis (2023.findings-acl)
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| Challenge: | In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. |
| Approach: | They use a BERT BASE model to mask the sentiments of an English dataset and find that both methods produce faithful rationales. |
| Outcome: | The proposed classifier outperforms both the gradient-based and black-box saliency methods on the SemEval 2016 english dataset. |
Argument-Based Sentiment Analysis on Forward-Looking Statements (2024.findings-acl)
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| Challenge: | Existing models for argument mining are limited in interpreting future-oriented arguments. |
| Approach: | They propose a categorization of argument units into claims, premises, and scenarios coupled with a unique sentiment analysis framework. |
| Outcome: | The proposed framework outperforms existing models in most tasks and is more efficient than existing methods. |
Sentiment Knowledge Enhanced Self-supervised Learning for Multimodal Sentiment Analysis (2023.findings-acl)
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| Challenge: | Existing studies have used general approaches to alleviate the overfitting of supervised models based on video data with sentiment annotations. |
| Approach: | They propose to capture common sentimental patterns in unlabeled videos using sentiment knowledge and non-verbal behavior to embed sentiment information into pre-trained multimodal representations. |
| Outcome: | The proposed model outperforms the baseline and achieves new State-Of-The-Art (SOTA) results. |
MultiEMO: An Attention-Based Correlation-Aware Multimodal Fusion Framework for Emotion Recognition in Conversations (2023.acl-long)
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| Challenge: | Emotion Recognition in Conversations (ERC) is an increasingly popular task in the field of Natural Language Processing. |
| Approach: | They propose a framework that captures cross-modal mapping relationships across modalities . they propose 'multiemotion-aware' framework that integrates multimodal cues into the model . |
| Outcome: | The proposed framework outperforms state-of-the-art models in all emotion categories on two benchmark datasets. |
Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining (2020.lrec-1)
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| Challenge: | a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets . |
| Approach: | They propose a meaning-preserving annotation scheme for aspect-based sentiment analysis . they then apply it to two popular ABSA datasets to examine their results . |
| Outcome: | The proposed approach improves the state of aspect-based sentiment analysis (ABSA) by preserving the meaning of the sentiment. |
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art results on all datasets. |
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression (2023.acl-long)
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| Challenge: | Existing methods to predict human emotions are inconsistent due to complexity of emotion and subjectivity of perception. |
| Approach: | They propose a Bayesian approach to estimate uncertainty in emotion attributes using a deep neural network model. |
| Outcome: | The proposed approach estimates uncertainty in emotion attributes along with aleatoric and epistemic uncertainties. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
WorryWords: Norms of Anxiety Association for over 44k English Words (2024.emnlp-main)
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| Challenge: | Anxiety is a common and beneficial human emotion, but there is still much that is not known about it . |
| Approach: | They propose a repository of manually derived word–anxiety associations for over 44,450 English words. |
| Outcome: | The proposed system can track anxiety in streams of text using WorryWords alone. |
Error Comparison Optimization for Large Language Models on Aspect-Based Sentiment Analysis (2025.acl-long)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) only compare current predictions and labels on each sample, yet fail to perceive and understand its error outputs from different degrees. |
| Approach: | They propose a framework that can perceive and understand the degree of errors by learning from comparative error pairs. |
| Outcome: | The proposed framework exceeds baselines and achieves the desired performance. |
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets (2024.lrec-main)
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| Challenge: | Existing studies on sentiment analysis of tweets focus on the English language . however, there is still a challenge of processing lower-resourced languages . |
| Approach: | They transform tweet sentiment dataset into a multimodal format through a straightforward curation process. |
| Outcome: | The proposed approach performs exceptionally well in unimodal and multimodal configurations. |
Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition (2025.emnlp-main)
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Danielle Cohen, Yoni Halpern, Noam Kahlon, Joel Oren, Omri Berkovitch, Sapir Caduri, Ido Dagan, Anatoly Efros
| Challenge: | Large multi-modal large language models are good at extracting user intents from UI sequences, but smaller models struggle with accurate intent inference. |
| Approach: | They propose a decomposed approach for extracting user intent from small models . they perform structured interaction summarization and intent extraction using a fine-tuned model . |
| Outcome: | The proposed method surpasses the performance of large MLLMs in the intent extraction task. |
Dual-oriented Disentangled Network with Counterfactual Intervention for Multimodal Intent Detection (2024.emnlp-main)
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| Challenge: | Existing methods for multimodal intent detection have two limitations: (i) close entanglement of multimodal semantics with modal structures; (ii) insufficient learning of causal effects of semantic and modality-specific information on the final predictions. |
| Approach: | They propose a Dual-oriented Disentangled Network with Counterfactual Intervention model that decouples semantics-oriented and modality-oriented representations and a Counterfective Intervention Module that applies causal inference to understand causal effects by injecting confounders. |
| Outcome: | The proposed model overcomes key limitations in existing systems by effectively disentangling and utilizing modality-specific and multimodal semantic information. |
Metaphors in Online Religious Communication: A Detailed Dataset and Cross-Genre Metaphor Detection (2024.lrec-main)
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| Challenge: | figurative language plays a particularly important role in religious communication . linguistic metaphors relate entities from different semantic domains by drawing on an implicit similarity between them. |
| Approach: | They present a dataset of fine-grained metaphor annotations for online religious communication . they show that cross-genre transfer metaphor detection leads to a drop in performance . |
| Outcome: | The proposed dataset shows that adding in-genre data improves performance . the authors show that the proposed system can detect metaphors in religious forums . |
(RSA)²: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding (2025.acl-long)
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| Challenge: | Existing implementations of the Rational Speech Act (RSA) framework do not account for figurative expressions or require modeling the implicit motivations behind using figurativ language in a setting-specific way. |
| Approach: | They propose a framework which models figurative language use by considering a speaker's employed rhetorical strategy and a computational model which incorporates rhetorical strategies to support non-literal interpretation. |
| Outcome: | The proposed framework enables human-compatible interpretations of non-literal utterances without modeling speaker's motivations for being non-lative. |
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts (2024.lrec-main)
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| Challenge: | Existing methods for OOD intent detection are limited to single dialogue turns. |
| Approach: | They propose a context-aware OOD intent detection framework to model multi-turn contexts in OOD context detection tasks using unlabeled data. |
| Outcome: | The proposed framework improves the F1-OOD score by 29% on multi-turn OOD detection tasks compared to the previous best method. |
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish (2024.lrec-main)
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| Challenge: | Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. |
| Approach: | They propose to use customer opinions of hotels and purchased products in Polish to extract ASTE triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarities. |
| Outcome: | The proposed datasets contain customer opinions about hotels and purchased products expressed in Polish and are available under a permissive licence and have the same file format as the English datasets. |
PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization (2024.lrec-main)
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| Challenge: | Existing studies have focused on summarizing factual information, leaving out affective content. |
| Approach: | They propose to quantify the preservation of affective content in dialogue summaries using PSentScore measures. |
| Outcome: | The proposed measures show that state-of-the-art summarization models do not preserve well affective content in their summaries. |
Aspect-based Sentiment Analysis via Synthetic Image Generation (2025.findings-emnlp)
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| Challenge: | Recent advances in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. |
| Approach: | They propose a supervised image generation framework to generate synthetic images with alignment to text and sentiment information. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on multiple benchmark datasets. |
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News (2025.acl-long)
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| Challenge: | Current approaches to modeling individual behavior ignore individual differences in how people interpret and react to identical stimuli. |
| Approach: | They propose a large-scale dataset specifically designed to facilitate the modeling of personalized affective responses to news content. |
| Outcome: | The proposed dataset includes annotations from 291 demographically diverse UK participants across 2,899 multimodal Facebook news posts from major UK outlets. |
MERMAID: Multi-perspective Self-reflective Agents with Generative Augmentation for Emotion Recognition (2025.emnlp-main)
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| Challenge: | Existing multimodal large language models struggle to handle ambiguous emotional expressions and implicit affective cues, which are crucial for affective understanding but largely overlooked. |
| Approach: | They propose a multi-agent framework that integrates a self-reflection module, an emotion-guided visual augmentation module, and a cross-modal verification module to enhance emotion recognition. |
| Outcome: | Extensive experiments show that MERMAID outperforms existing methods and achieves absolute accuracy gains of 8.70%–27.90% across diverse benchmarks. |
Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing (2026.acl-long)
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| Challenge: | Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such success actually reveals about metaphor processing. |
| Approach: | They propose to probing semantic attribute alignment, lexical invariance, and syntactic sensitivity to examine the limits of behavioral evidence for metaphor processing. |
| Outcome: | The proposed model can exhibit semantic drift relative to reference attributes, stable lexical anchors persist across contextual conditions, potentially supporting conventional metaphors while biasing novel metaphors requiring contextual integration. |
LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight (2026.acl-long)
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Yu-Zheng Lin, Bono Po-Jen Shih, John Paul Martin Encinas, Elizabeth Victoria Achom, Karan Patel, Jesus Horacio Pacheco, Sicong Shao, Jyotikrishna Dass, Soheil Salehi, Pratik Satam
| Challenge: | Emotional coordination is a core property of human interaction that shapes relational meaning . prior approaches treat sentiment as a deterministic point estimate for individual speakers . scalable and deployable approach extends beyond education to broader social and behavioral research . |
| Approach: | They propose a probabilistic framework that characterizes emotion as a latent probability distribution defined over an affective space. |
| Outcome: | The proposed framework characterizes emotion as a latent probability distribution defined over affective space. |
PASTEL : Polarity-Aware Sentiment Triplet Extraction with LLM-as-a-Judge (2025.findings-acl)
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| Challenge: | Existing methods for extracting triplets of aspect terms and opinions are inadequate due to complexity of aspect-opinion interactions and implicit nature of sentiment dependencies in natural language. |
| Approach: | They propose a pipeline that decomposes the ASTE task into structured subtasks . they employ fine-tuned LLMs to separately extract the aspect and opinion terms . |
| Outcome: | The proposed pipeline outperforms existing baselines in the ASTE subtask. |
LLMs in Sarcasm Detection? It’s elementary! (Or is it?) (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are often cited for their sophisticated pragmatic reasoning, but they collapse to random guessing on organic human speech. |
| Approach: | They propose that LLMs have near-human competence in sarcasm detection . authors propose that this proficiency may be deceptive . |
| Outcome: | The proposed model performance on synthetic leaderboards is a statistical mirage of competence. |
The Impact of Stance Object Type on the Quality of Stance Detection (2024.lrec-main)
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| Challenge: | stance is defined by Biber and Finegan as the expression of an author's standpoint and judgment towards a given proposition. |
| Approach: | They analyze the implied knowledge and judgments required when deciding the stance of a text towards each possible stance object type. |
| Outcome: | The proposed models can infer the stance of a text towards any of the three stance object types, namely topics, claims, and frames of communication. |
Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are used for processing multiple documents or analysis over a number of instances. |
| Approach: | They perform a comprehensive evaluation of the multi-instance processing ability of LLMs for tasks in which they excel individually. |
| Outcome: | The proposed model performs well on tasks in which it excels individually. |
User Guide for KOTE: Korean Online That-gul Emotions Dataset (2024.lrec-main)
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| Challenge: | sentiment analysis is used to identify emotional aspects of texts but is limited by its small size and limited range of emotions. |
| Approach: | They propose a Korean sentiment analysis corpus that is limited by its small size and narrow range of emotions . they propose to fine-tune the KOTE dataset and analyze the results for social discrimination . |
| Outcome: | The proposed dataset includes 50,000 Korean online comments, each manually labeled for 43 emotions and NO EMOTION. |
When Do “More Contexts” Help with Sarcasm Recognition? (2024.lrec-main)
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| Challenge: | Prior work has focused on integrating more contexts into sarcasm recognition models, but no systematic evaluation of their collective effectiveness has been conducted. |
| Approach: | They propose a framework to integrate multiple contextual cues into a model and test different approaches. |
| Outcome: | The proposed framework achieves state-of-the-art performance and also shows the benefits of sequentially adding more contexts. |
ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization (2026.acl-long)
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| Challenge: | prevailing RLHF methods such as PPO and DPO depend on large-scale binary preference annotations. |
| Approach: | They propose a method which converts natural feedback into continuous preference trajectories and optimizes them using the novel TraceBias algorithm. |
| Outcome: | The proposed approach outperforms PPO and DPO in a variety of domains and improves alignment by up to 7.6% across diverse LLMs and preference domains. |
A Text-Based Recommender System that Leverages Explicit Affective State Preferences (2025.emnlp-main)
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| Challenge: | Existing systems that leverage user preferences that are implicit in user-item rating histories can be slow to track changes in user preferences and imprecise for users with diverse preferences. |
| Approach: | They propose a novel recommendation task that leverages a wide range of affective states sought explicitly by the user to identify items that induce those affective state. |
| Outcome: | The proposed model can leverage a wide range of affective states sought explicitly by the user to identify items likely to induce those affective state. |
SUE: Sparsity-based Uncertainty Estimation via Sparse Dictionary Learning (2025.emnlp-main)
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| Challenge: | Existing methods to estimate uncertainty use predictive confidence, structural characteristics of representation space, or stochastic variation in model outputs. |
| Approach: | They propose a new uncertainty estimation framework based on sparse dictionary learning by identifying dictionary atoms associated with misclassified samples. |
| Outcome: | The proposed framework outperforms or matches existing methods on several NLU benchmarks and sentiment analysis benchmarks. |
Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses (2026.acl-long)
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Chongyuan Dai, Yaling Shen, Zihan Gao, Jia Li, Yishun Jiang, Yaxiong Wang, Liu Liu, Zongyuan Ge, Jinpeng Hu
| Challenge: | Culture is a fundamental determinant of human affective processing and affective perceptions are often limited by declarative knowledge or established societal customs. |
| Approach: | They propose a multimodal benchmark that leverages LLM-generated provisional labels to isolate cross-cultural emotional distinctions. |
| Outcome: | The proposed benchmark captures cross-cultural emotional distinctions and derives reliable ground-truth annotations through human evaluation. |
Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)
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| Challenge: | Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings. |
| Approach: | They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions. |
| Outcome: | The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset. |
TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection (2026.acl-long)
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| Challenge: | Existing benchmarks for political user-level stance detection rely on noisy heuristics or distant supervision. |
| Approach: | They propose a large-scale, expert-annotated benchmark for political user-level stance detection with explicit social network structure that integrates user content and followee signals. |
| Outcome: | The proposed framework outperforms baselines in terms of quality and reliability. |
Characterizing and Evaluating Working Emotion Vocabularies in Multilingual Large Language Models (2026.acl-long)
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| Challenge: | Prior work evaluating emotion and affective understanding in large language models rely on predetermined label sets or focus on a singular evaluation task. |
| Approach: | They examine the ability of multilingual language models to predict any term used by an author to label their own feelings or emotions. |
| Outcome: | The proposed models perform poorly on three different tasks in English and Spanish. |