SENTIMENT ANALYSIS

From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)

Challenge:	Existing methods for sentiment analysis of user reviews are limited to a few examples.
Approach:	They propose a hierarchically-refined attention model that exploits the sentimental distribution of a review and its corresponding summary.
Outcome:	The proposed model can make better use of user-written summaries for review sentiment analysis and is more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

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Patrick Huber, Giuseppe Carenini

Sentiment Analysis of Yelp Review Dataset: A Comparative Study of Machine Learning Methods (2026.acl-srw)

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.

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Krishna Thakar, Mohamed Abu Sheha, Emmanuel Thompson

A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (2022.acl-srw)

Challenge:	Existing methods for sentiment analysis are inconsistent and require manual processing.
Approach:	They use natural language processing and machine learning to classify Yelp reviews' sentiments.
Outcome:	The proposed model outperforms other models on Yelp reviews.

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Mustafa Melih Mutlu, Arzucan Özgür

A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (2022.findings-naacl)

Challenge:	Sentiment analysis is a field that is growing due to the availability of the Internet and the growing number of online platforms.
Approach:	They propose an annotated Turkish dataset suitable for targeted sentiment analysis.
Outcome:	The proposed models outperform the traditional models for the targeted sentiment analysis task.

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Ehsan Hosseini-Asl, Wenhao Liu, Caiming Xiong

Multi-source Multi-domain Sentiment Analysis with BERT-based Models (2022.lrec-1)

Challenge:	Pre-trained language models are often used to achieve state-of-the-art results . eval paper shows that generative language model can handle joint and multi-task settings .
Approach:	They propose to reformulate extraction and prediction tasks into a sequence generation task . they propose a generative language model with unidirectional attention that learns to accomplish the tasks via language generation .
Outcome:	The proposed model outperforms the state-of-the-art in few-shot and full-shot settings.

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Gabriel Roccabruna, Steve Azzolin, Giuseppe Riccardi

NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis (2022.lrec-1)

Challenge:	Sentiment analysis is a widely studied task in natural language processing.
Approach:	They propose to improve BERT-based models for sentiment analysis on italian corpora and evaluate their performance on the basis of eight corpors.
Outcome:	The proposed model is evaluated over eight sentiment analysis corpora from different domains and sources on the prediction of positive, negative and neutral classes.

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Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Sa’id Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alípio Jorge, Pavel Brazdil

Towards Robust Sentiment Analysis of Temporally-Sensitive Policy-Related Online Text (2025.acl-srw)

Challenge:	Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data.
Approach:	They propose a large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria.
Outcome:	The proposed dataset includes 30,000 tweets and a significant fraction of code-mixed tweets.

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Charles Alba, Benjamin C Warner, Akshar Saxena, Jiaxin Huang, Ruopeng An

Robustness in NLP

Challenge:	Existing methods fail to adequately capture the temporal volatility inherent in policy-related sentiments, arguing that continuous time-series clustering and model merging achieve superior performance.
Approach:	They propose to use continuous time-series clustering to select data points for annotation based on temporal trends and then apply model merging techniques.
Outcome:	The proposed methods outperform existing methods by an average F1-score of 2.71% on temporally representative data.

Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks (2025.emnlp-main)

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Sarfaroz Yunusov, Kaige Chen, Kazi Nishat Anwar, Ali Emami

Self-training Strategies for Sentiment Analysis: An Empirical Study (2024.findings-eacl)

Challenge:	Large Language Models (LLMs) are increasingly integrated into everyday workflows . a recent study found that LLMs exhibit distinct personality-like traits that affect user engagement .
Approach:	They evaluated 32 LLM users for four collaborative tasks and found significant preferences . they found that rationalists preferred GPT-4, while idealists favored Claude 3.5 .
Outcome:	The results show that users with different personality traits prefer certain LLMs over others.

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Haochen Liu, Sai Rallabandi, Yijing Wu, Parag Dakle, Preethi Raghavan

ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)

Challenge:	Sentiment analysis is a crucial task in natural language processing.
Approach:	They propose to leverage a small amount of labeled and unlabeled data to train models with self-training.
Outcome:	The proposed method improves the performance of small language models in several few-shot settings while reducing the cost of annotations.

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Jiahao Bu, Lei Ren, Shuang Zheng, Yang Yang, Jingang Wang, Fuzheng Zhang, Wei Wu

Sentiment Analysis: It’s Complicated! (N18-1)

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.

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Kian Kenyon-Dean, Eisha Ahmed, Scott Fujimoto, Jeremy Georges-Filteau, Christopher Glasz, Barleen Kaur, Auguste Lalande, Shruti Bhanderi, Robert Belfer, Nirmal Kanagasabai, Roman Sarrazingendron, Rohit Verma, Derek Ruths

SentiArabic: A Sentiment Analyzer for Standard Arabic (L18-1)

Challenge:	a dataset of over 7,000 tweets annotated with 5x coverage is used for sentiment analysis . a "complicated" class of sentiment is used to categorize text based on a predefined notion of sentiment .
Approach:	They propose to use a "complicated" class of sentiment to categorize tweets . they build a publicly available tweet sentiment analysis dataset .
Outcome:	The proposed classifiers perform better over a new publicly available TSA dataset . the classifier performance is compared with existing methods and improves on existing ones .

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Ramy Eskander

Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025.findings-emnlp)

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.

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Hongfei Du, Jiacheng Shi, Jacobo Myerston, Sidi Lu, Gang Zhou, Ashley Gao

Information Retrieval

Challenge:	Prior work focused on using sentiment lexicons or leveraging large language models for annotation . lexiconics are often unavailable for historical texts due to limited linguistic resources .
Approach:	They propose a role-guided annotation strategy that prompts LLMs to simulate historical perspectives when labeling sentiment.
Outcome:	The proposed method outperforms state-of-the-art baselines across historical literature datasets.

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

Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages (P18-1)

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 .

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Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde

Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)

Challenge:	Existing approaches to sentiment analysis in low-resource languages lack annotated corpora or do not capture sentiment information.
Approach:	They propose a model that represents sentiment in a source and target language without annotated corpus.
Outcome:	The proposed model outperforms state-of-the-art methods on four out of six setups and captures complementary information to machine translation.

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Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Pan, Lidong Bing

Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages (2023.findings-acl)

Challenge:	Sentiment analysis (SA) has been a long-standing research area in natural language processing.
Approach:	They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation.
Outcome:	The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets.

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Seraphina Goldfarb-Tarrant, Adam Lopez, Roi Blanco, Diego Marcheggiani

Variational Weakly Supervised Sentiment Analysis with Posterior Regularization (2021.eacl-main)

Challenge:	Sentiment analysis systems are used in hundreds of products and languages . Gender and racial biases are well-studied in English, but understudied elsewhere .
Approach:	They build a counterfactual evaluation corpus for gender and racial/migrant bias in four languages.
Outcome:	The evaluation corpus reveals which models have less bias and pinpoints changes in model bias behaviour, enabling more targeted mitigation strategies.

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Ziqian Zeng, Yangqiu Song

Responsible & Trustworthy NLP

Challenge:	Existing methods for sentiment analysis require human annotations, but they are scarce.
Approach:	They propose a posterior regularization framework to control the posterior distribution of label assignment.
Outcome:	The proposed framework improves the variational approach to the weakly supervised sentiment analysis and the performance is more stable with smaller prediction variance.

Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis (2023.emnlp-main)

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Seraphina Goldfarb-Tarrant, Björn Ross, Adam Lopez

IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)

Challenge:	Existing evidence of demographic bias in SA systems is limited to a handful of languages, and it is costly to create supervised training data in a new language.
Approach:	They use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer . r&r is much more prevalent than gender biase .
Outcome:	The proposed model is compared with monolingual systems in five languages and shows that it is biased more than monolingual ones.

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Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, Asif Ekbal

Crowdsourcing and Validating Event-focused Emotion Corpora for German and English (P19-1)

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.

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Enrica Troiano, Sebastian Padó, Roman Klinger

MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations (2020.coling-main)

Challenge:	Existing studies on automatic recognition of emotions in text have achieved promising results, but there is a shortage of resources for non-English languages, with few exceptions, like Chinese.
Approach:	They propose to use a crowdsourced German emotion corpus to build a corpus similar to the English ISEAR emotion dataset.
Outcome:	The proposed model performs well in German and English, but lacks the resources for non-English languages.

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Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, Pushpak Bhattacharyya

IndiSentiment140: Sentiment Analysis Dataset for Indian Languages with Emphasis on Low-Resource Languages using Machine Translation (2024.naacl-long)

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.

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Saurabh Kumar, Ranbir Sanasam, Sukumar Nandi

Machine Translation

Challenge:	Existing solutions to bridge the gap between resource-rich and resource-poor languages are being explored.
Approach:	They examine the feasibility of machine translation for creating sentiment analysis datasets in 22 Indian languages.
Outcome:	The proposed dataset can be used to tackle low-resource challenges in sentiment analysis for Indian languages.

Sentiment Analysis on Streaming User Reviews via Dual-Channel Dynamic Graph Neural Network (2023.emnlp-main)

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Xin Zhang, Linhai Zhang, Deyu Zhou

Multimodality

Challenge:	Existing methods for sentiment analysis on user reviews neglect their time-varying characteristics.
Approach:	They propose a dual-channel framework that models temporal user and product dynamics for sentiment analysis.
Outcome:	The proposed framework is superior to existing methods on five real-world datasets.

Cross-Lingual Word Embeddings for Turkic Languages (2020.lrec-1)

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Elmurod Kuriyozov, Yerai Doval, Carlos Gómez-Rodríguez

SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)

Challenge:	Existing techniques to align monolingual embeddings are difficult to use because of low resources.
Approach:	They propose to use existing techniques to align monolingual embedding spaces for Turkic, Uzbek, Azeri, Kazakh and Kyrgyz languages.
Outcome:	The proposed techniques outperform existing techniques on bilingual dictionaries and an extrinsic task.

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Yue Deng, Wenxuan Zhang, Sinno Pan, Lidong Bing

Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis (2024.findings-emnlp)

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% .

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Zhiheng Lyu, Zhijing Jin, Fernando Gonzalez Adauto, Rada Mihalcea, Bernhard Schölkopf, Mrinmaya Sachan

Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)

Challenge:	Sentiment analysis aims to identify the sentiment expressed in a piece of text, often in the form of a review.
Approach:	They propose a causal discovery task that distinguishes whether a review "primes" the sentiment and a traditional prediction task to model the sentiment using the review as input.
Outcome:	The proposed model improves by 32.13 F1 points on a zero-shot five-class SA.

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SM Mazharul Islam, Xin Dong, Gerard de Melo

Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)

Challenge:	Existing sentiment lexicons reflect abstract notion of polarity and do not do justice to substantial differences of word polarities between domains.
Approach:	They propose to use domain-specific sentiment lexicons to induce initial word intensity scores and train new deep models based on word vector representations to overcome the scarcity of the seed data.
Outcome:	The proposed models show that they perform well on review classification and cross-lingual word sentiment prediction.

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Xulang Zhang, Rui Mao, Kai He, Erik Cambria

Explainability & Interpretability in NLP

Challenge:	Traditional neural network models represent word senses as vectors that are uninterpretable for humans.
Approach:	They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions.
Outcome:	The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels.

Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (2022.coling-1)

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Feng Ge, Weizhao Li, Haopeng Ren, Yi Cai

KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)

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.

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Minghao Xu, Daling Wang, Shi Feng, Zhenfei Yang, Yifei Zhang

Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora (2024.lrec-main)

Challenge:	Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words.
Approach:	They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression .
Outcome:	The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset.

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Iben Nyholm Debess, Annika Simonsen, Hafsteinn Einarsson

Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)

Challenge:	Existing studies on sentiment analysis in low-resource languages have focused on major languages and emotionally laden text genres like social media and reviews.
Approach:	They propose to use GPT-4 for sentiment analysis on Faroese news texts using a multi-class approach with 225 sentences analysed in 170 articles.
Outcome:	The proposed model performs remarkably well on 225 sentences and 170 articles compared to human annotators .

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Taha Shangipour ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, Sauleh Eetemadi