Papers with Sentiment Analysis
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets (N18-4)
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| Challenge: | Existing word embeddings for sentiment analysis are limited in domain specific applications . generic word embeds are poor initialization for tasks on domain specific data sets. |
| Approach: | They propose to use word embeddings adapted for domain specific data sets in sentiment classification applications. |
| Outcome: | The proposed algorithms learn word embeddings on sparse and sentiment rich data sets. |
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (2022.acl-srw)
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| 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. |
No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP (L18-1)
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| Challenge: | idioms are a part of natural language and are difficult to learn with a parallel corpora database. |
| Approach: | They propose to use a parallel idiom dataset to train two NLP subtasks . they show significant improvement in the two subtask training without the idiomatic dataset . |
| Outcome: | The proposed model improves on baseline models with the idiom dataset for two NLP applications. |
De-Mixing Sentiment from Code-Mixed Text (P19-2)
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| Challenge: | Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. |
| Approach: | They propose a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data using CNNs to generate subword representations for the sentences. |
| Outcome: | The proposed architecture achieves 83.54% accuracy and 0.827 F1 score on a benchmark dataset. |
Improving Pretraining Techniques for Code-Switched NLP (2023.acl-long)
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| Challenge: | Multilingual pretraining models for code-switched inputs are a key component of NLP applications. |
| Approach: | They propose to use masked language modeling techniques to mask code-switched text that are cognizant of language boundaries prior to masking. |
| Outcome: | The proposed techniques improve performance on two downstream tasks, Question Answering (QA) and Sentiment Analysis (SA), compared to standard pretraining techniques. |
Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction (L18-1)
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| Challenge: | Sentiment Analysis of text is an important task in many applications . but the task becomes challenging when it comes to low resource languages . |
| Approach: | They propose to create a corpus of polarity-based sentiment classifiers in Telugu for different domains like movie reviews, song lyrics, product reviews and book reviews. |
| Outcome: | The proposed model performs well in multiple domains and is compared with the previous models. |
EnerGIZAr: Leveraging GIZA++ for Effective Tokenizer Initialization (2025.findings-acl)
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| Challenge: | Continual pre-training has long been considered the default strategy for adapting models to non-English languages, but struggles with initializing new embeddings, especially for non-Latin scripts. |
| Approach: | They propose a method that leverages statistical word alignment techniques to improve continual pre-training by leveraging word alignment matrix between source and target tokens. |
| Outcome: | The proposed method outperforms existing methods on key NLP tasks including POS tagging, Sentiment Analysis, NLI, and NER in Hindi, Basque, Arabic and Korean. |
Cooperative Learning of Disjoint Syntax and Semantics (N19-1)
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| Challenge: | Existing models that learn to jointly infer an expression’s syntactic structure and its semantics fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. |
| Approach: | They propose a recursive model that learns to jointly infer an expression’s syntactic structure and its semantics without requiring a formal supervision. |
| Outcome: | The proposed model performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis. |
uniblock: Scoring and Filtering Corpus with Unicode Block Information (D19-1)
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| Challenge: | Existing methods to remove sentences consisting of illegal characters are tedious and repetitive. |
| Approach: | They propose a statistical method to identify illegal characters in natural language processing . they use a fixed-size feature vector to generate a Gaussian mixture model for each sentence . |
| Outcome: | The proposed method can score sentences and filter corpus on clean corpus and improve performance. |
Locally Aggregated Feature Attribution on Natural Language Model Understanding (2022.naacl-main)
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| Challenge: | a growing popularity of deep-learning models makes model understanding more important . feature attribution methods have shown promising results in computer vision but are not trivial . |
| Approach: | They propose a gradient-based feature attribution method that smooths gradients by aggregating similar reference texts derived from language model embeddings. |
| Outcome: | The proposed method outperforms existing methods on public datasets and key words detection tasks. |
Multi-Domain Targeted Sentiment Analysis (2022.naacl-main)
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| Challenge: | Targeted Sentiment Analysis (TSA) is a task for generating insights from consumer reviews. |
| Approach: | They propose a multi-domain TSA system that augments a given training set with diverse weak labels from assorted domains and augments it with Yelp reviews. |
| Outcome: | The proposed model outperforms manual methods on three evaluation datasets across different domains and shows that it performs well. |
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. |
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains. (C18-1)
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| Challenge: | Standard word embedding algorithms learn vector representations from large corpora of text documents in unsupervised fashion. |
| Approach: | They propose an algorithm that learns word embeddings jointly with a classifier . their algorithm leverages document label information to learn vector representations of words . |
| Outcome: | The proposed algorithm has superior performance on domains with limited data compared to other methods. |
GLUECoS: An Evaluation Benchmark for Code-Switched NLP (2020.acl-main)
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| Challenge: | Recent studies show multilingual contextual embedding models perform better on cross-lingual and multilingual tasks. |
| Approach: | They propose to evaluate multilingual contextual embedding models on multilingual data . they use language identification from text, POS tagging, Named Entity Recognition and Question Answering . |
| Outcome: | The proposed benchmark evaluates models on language identification from text, POS tagging, Named Entity Recognition, Question Answering and a new task for code-switching, Natural Language Inference. |
Exploring Alignment in Shared Cross-lingual Spaces (2024.acl-long)
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| Challenge: | a new study examines the degree of alignment between languages in multilingual embeddings . cross-lingual embeds are designed to encode linguistic concepts that bridge equivalent semantic meaning . a comprehensive approach is needed to address these questions. |
| Approach: | They employ clustering to uncover latent concepts within multilingual models . they introduce two metrics to quantify alignment and overlap of these concepts . |
| Outcome: | The proposed model can capture linguistic nuances across languages, but is not language-agnostic? the proposed model is able to capture nuances in multiple languages, the authors say. |
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning (2023.emnlp-main)
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| Challenge: | Large language models have demonstrated their capability with few-shot inference . however, in-domain demonstrations are not always available in real scenarios . |
| Approach: | They propose unsupervised domain adaptation problem to adapt language models from source domain to target domain without any target labels. |
| Outcome: | The proposed model performs better than baseline models on Sentiment Analysis and Named Entity Recognition tasks. |
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free Approach (2023.findings-emnlp)
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| Challenge: | Shortcut reasoning is an irrational process of inference, which degrades the robustness of an NLP model. |
| Approach: | They propose a method to quantify the severity of shortcut reasoning by leveraging out-of-distribution data. |
| Outcome: | The proposed method quantifies the severity of the discovered shortcut reasoning using out-of-distribution data. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset. |
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study (D18-1)
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| Challenge: | Existing methods to evaluate deep learning models that are not considered for test set accuracy are difficult to interpret. |
| Approach: | They examine the impact of a test set question’s difficulty to determine if there is a relationship between difficulty and performance. |
| Outcome: | The proposed model can learn examples of varying difficulty at different rates if it does well on hard examples and poor on easy items because a dataset is all easy, but has "solved" anything? |
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer (2021.emnlp-main)
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| Challenge: | Comparative Preference Classification (CPC) is a natural language processing task that predicts whether a preference comparison exists between two entities in a given sentence . |
| Approach: | They propose a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. |
| Outcome: | Experiments on the CompSent-19 dataset present a significant improvement on the F1 scores over the best existing CPC approaches. |
Standardisation of Dialect Comments in Social Networks in View of Sentiment Analysis : Case of Tunisian Dialect (2022.lrec-1)
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| Challenge: | Using the internet, the spoken Arabic dialect language becomes informal languages written in social media . this linguistic situation inhibits mutual understanding and makes computational approaches difficult . we present a pipeline to standardize the written texts in social networks by translating them to MSA . |
| Approach: | They propose a pipeline to standardize Arabic written texts by translating them to MSA . they use a bert-based model to select Tunisian Dialect from MSA and other dialects . |
| Outcome: | The proposed pipeline achieves the best score for the standardization of written texts in social networks . the proposed pipeline includes the translated TD and the original text written in MSA . |
A Corpus for Suggestion Mining of German Peer Feedback (2022.lrec-1)
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| Challenge: | e.g. Massive Open Online Courses (MOOCs) are increasingly important to meet the demand for feedback in large scale classes. |
| Approach: | They propose to use peer feedback to detect suggestions on how to improve the work of students in a german university course. |
| Outcome: | The proposed corpus is the first student peer feedback corpus in germany and has been labelled with a new annotation scheme. |
Manovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus (2020.lrec-1)
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| Challenge: | Several studies conducted on the different styles of reporting in journalism are essential in understanding phenomena such as media bias and multiple interpretations of the same event. |
| Approach: | They propose a novel method of event reporting that correlates the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model. |
| Outcome: | The proposed method correlates the degree of subjectivity with the geographical closeness of reporting using a Bi-RNN model. |
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. |
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. |
Attention-Enhancing Backdoor Attacks Against BERT-based Models (2023.findings-emnlp)
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| Challenge: | Existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights. |
| Approach: | They propose a Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating attention patterns. |
| Outcome: | The proposed method improves the effectiveness of the backdoor attacks on different backbone models and tasks. |
The MERSA Dataset and a Transformer-Based Approach for Speech Emotion Recognition (2024.acl-long)
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| Challenge: | Existing models for speech emotion recognition lack a comprehensive dataset to design accurate models. |
| Approach: | They propose to use a multimodal dataset to build a model that integrates pre-trained wav2vec 2.0 and BERT to learn hidden representations from fused representations of speech and text. |
| Outcome: | The proposed model predicts emotions on dimensions of arousal, valence, and dominance . it achieved competitive results on the MSP-PODCAST dataset . |
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. |
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. |
CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing (2020.lrec-1)
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Ossama Obeid, Nasser Zalmout, Salam Khalifa, Dima Taji, Mai Oudah, Bashar Alhafni, Go Inoue, Fadhl Eryani, Alexander Erdmann, Nizar Habash
| Challenge: | CAMeL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Approach: | They present CAMeL Tools, an open-source Python toolkit for Arabic natural language processing . CAMeleL Tools provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and sentiment analysis. |
| Outcome: | The proposed tools are based on CAMeL Tools, an open-source Python toolkit for Arabic natural language processing. |
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance in NLP tasks, but their efficacy in generating high-quality CFs remains uncertain. |
| Approach: | They compare LLMs' ability to generate CFs that flip the original label and human CF's. |
| Outcome: | The proposed models generate fluent CFs, but struggle to keep the induced changes minimal. |
New Evaluation Methodology for Qualitatively Comparing Classification Models (2024.lrec-main)
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| Challenge: | Text Classification is one of the most common tasks in Natural Language Processing. |
| Approach: | They propose a method for performing qualitative assessment over multiple classification models using a fine-tuned BERT and Logistic Regression evaluation methodology. |
| Outcome: | The proposed evaluation methodology outperforms the baseline model in linguistic clustering and Sentiment Analysis. |
An Experimental Study on the Influence of Culture on Cross-Lingual Sentiment Transfer (2026.acl-long)
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| Challenge: | Identical linguistic expressions can convey different sentiments across cultural contexts . current multilingual models often reduce language to symbolic representation . cultural misalignment is a structural bottleneck, authors say . |
| Approach: | They conduct an empirical study to quantify the influence of culture on cross-lingual sentiment transfer across 7 common SMLMs and 5 linguistically diverse languages. |
| Outcome: | The proposed model disentangles cultural factors from confounding variables and shows cultural distance is a negative predictor of transfer performance. |