Sentiment Analysis

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Papers

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Showing 651–675 of 5630 papers

Title	Date	Tasks	Status
An Eye-tracking Study of Named Entity Annotation	Sep 1, 2017	Active LearningCoreference Resolution	—Unverified
Affective Common Sense Knowledge Acquisition for Sentiment Analysis	May 1, 2012	Common Sense ReasoningDecision Making	—Unverified
An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection	Jul 20, 2022	Sentiment Analysis	—Unverified
Affection Driven Neural Networks for Sentiment Analysis	May 1, 2020	Sentiment Analysis	—Unverified
A Case Study of Machine Translation in Financial Sentiment Analysis	Sep 1, 2017	Machine TranslationSentiment Analysis	—Unverified
AUEB: Two Stage Sentiment Analysis of Social Network Messages	Aug 1, 2014	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	—Unverified
An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics	Sep 1, 2015	Machine TranslationNatural Language Inference	—Unverified
An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter	Dec 1, 2012	Named Entity Recognition (NER)Sentiment Analysis	—Unverified
Affect in Tweets Using Experts Model	Mar 20, 2019	Mixture-of-Expertsmodel	—Unverified
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets	Jun 1, 2018	Emotion ClassificationGeneral Classification	—Unverified
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''	Apr 1, 2021	Emotion RecognitionSentiment Analysis	—Unverified
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM	Oct 31, 2022	Chinese Sentiment AnalysisSentiment Analysis	—Unverified
Audio-Guided Fusion Techniques for Multimodal Emotion Analysis	Sep 8, 2024	Emotion RecognitionSelf-Supervised Learning	—Unverified
A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis	Jun 20, 2019	Sentiment Analysis	—Unverified
A New Approach To Text Rating Classification Using Sentiment Analysis	Mar 23, 2021	ClassificationGeneral Classification	—Unverified
Aff2Vec: Affect--Enriched Distributional Word Representations	May 21, 2018	Natural Language UnderstandingSentiment Analysis	—Unverified
An Evaluation of the Brazilian Portuguese LIWC Dictionary for Sentiment Analysis	Jan 1, 2013	Opinion MiningSentiment Analysis	—Unverified
A context-based model for Sentiment Analysis in Twitter	Aug 1, 2014	Sentiment AnalysisText Classification	—Unverified
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis	Aug 3, 2024	Emotion RecognitionSentiment Analysis	—Unverified
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach	Aug 7, 2020	Sentiment Analysis	—Unverified
An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses	Mar 27, 2025	Sentiment AnalysisTranslation	—Unverified
A Concrete Chinese NLP Pipeline	Jun 1, 2015	Coreference ResolutionEntity Linking	—Unverified
An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing	Aug 1, 2018	LemmatizationSentiment Analysis	—Unverified
A Neural Network Model for Low-Resource Universal Dependency Parsing	Sep 1, 2015	Dependency ParsingDomain Adaptation	—Unverified

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All datasets SST-2 Binary classification IMDb SST-5 Fine-grained classification Yelp Binary classification MR Yelp Fine-grained classification BanglaBook DynaSent SST-3 Sentiment Merged User and product information Amazon Review Full

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Word+ES (Scratch)	Attack Success Rate	100	—	Unverified
2	MT-DNN-SMART	Accuracy	97.5	—	Unverified
3	T5-11B	Accuracy	97.5	—	Unverified
4	MUPPET Roberta Large	Accuracy	97.4	—	Unverified
5	T5-3B	Accuracy	97.4	—	Unverified
6	ALBERT	Accuracy	97.1	—	Unverified
7	StructBERTRoBERTa ensemble	Accuracy	97.1	—	Unverified
8	XLNet (single model)	Accuracy	97	—	Unverified
9	SMARTRoBERTa	Dev Accuracy	96.9	—	Unverified
10	ELECTRA	Accuracy	96.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-large with LlamBERT	Accuracy	96.68	—	Unverified
2	RoBERTa-large	Accuracy	96.54	—	Unverified
3	XLNet	Accuracy	96.21	—	Unverified
4	Heinsen Routing + RoBERTa Large	Accuracy	96.2	—	Unverified
5	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	96.1	—	Unverified
6	GraphStar	Accuracy	96	—	Unverified
7	DV-ngrams-cosine with NB sub-sampling + RoBERTa.base	Accuracy	95.94	—	Unverified
8	DV-ngrams-cosine + RoBERTa.base	Accuracy	95.92	—	Unverified
9	Roberta_Large ST + Cosine Similarity Loss	Accuracy	95.9	—	Unverified
10	BERT large finetune UDA	Accuracy	95.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Llama-3.3-70B + CAPO	Accuracy	62.27	—	Unverified
2	Mistral-Small-24B + CAPO	Accuracy	60.2	—	Unverified
3	Heinsen Routing + RoBERTa Large	Accuracy	59.8	—	Unverified
4	RoBERTa-large+Self-Explaining	Accuracy	59.1	—	Unverified
5	Qwen2.5-32B + CAPO	Accuracy	59.07	—	Unverified
6	Heinsen Routing + GPT-2	Accuracy	58.5	—	Unverified
7	BCN+Suffix BiLSTM-Tied+CoVe	Accuracy	56.2	—	Unverified
8	BERT Large	Accuracy	55.5	—	Unverified
9	LM-CPPF RoBERTa-base	Accuracy	54.9	—	Unverified
10	BCN+ELMo	Accuracy	54.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Char-level CNN	Error	4.88	—	Unverified
2	SVDCNN	Error	4.74	—	Unverified
3	LEAM	Error	4.69	—	Unverified
4	fastText, h=10, bigram	Error	4.3	—	Unverified
5	SWEM-hier	Error	4.19	—	Unverified
6	SRNN	Error	3.96	—	Unverified
7	M-ACNN	Error	3.89	—	Unverified
8	DNC+CUW	Error	3.6	—	Unverified
9	CCCapsNet	Error	3.52	—	Unverified
10	Block-sparse LSTM	Error	3.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Millions of Emoji	Training Time	1,500	—	Unverified
2	VLAWE	Accuracy	93.3	—	Unverified
3	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	92.5	—	Unverified
4	AnglE-LLaMA-7B	Accuracy	91.09	—	Unverified
5	byte mLSTM7	Accuracy	86.8	—	Unverified
6	MEAN	Accuracy	84.5	—	Unverified
7	RNN-Capsule	Accuracy	83.8	—	Unverified
8	Capsule-B	Accuracy	82.3	—	Unverified
9	SuBiLSTM-Tied	Accuracy	81.6	—	Unverified
10	USE_T+CNN	Accuracy	81.59	—	Unverified