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 5001–5025 of 5630 papers

Title	Date	Tasks	Status
Class Vectors: Embedding representation of Document Classes	Aug 2, 2015	General ClassificationNatural Language Understanding	—Unverified
CLEAR: Contrastive Learning for Sentence Representation	Dec 31, 2020	Contrastive LearningLinguistic Acceptability	—Unverified
CLex: A Lexicon for Exploring Color, Concept and Emotion Associations in Language	Apr 1, 2012	Machine TranslationNatural Language Inference	—Unverified
ClimaText: A Dataset for Climate Change Topic Detection	Dec 1, 2020	Fact CheckingQuestion Answering	—Unverified
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations	Sep 1, 2015	DenoisingDomain Adaptation	—Unverified
Cloze-driven Pretraining of Self-attention Networks	Mar 19, 2019	Constituency ParsingNamed Entity Recognition (NER)	—Unverified
CLUF: a Neural Model for Second Language Acquisition Modeling	Jun 1, 2018	DecoderKnowledge Tracing	—Unverified
CluSent – Combining Semantic Expansion and De-Noising for Dataset-Oriented Sentiment Analysis of Short Texts	Nov 16, 2021	Sentiment Analysis	—Unverified
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation	Apr 1, 2014	Recommendation SystemsSentiment Analysis	—Unverified
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency	Oct 1, 2014	ClusteringSentiment Analysis	—Unverified
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set	Apr 1, 2021	ClusteringSentiment Analysis	—Unverified
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations	Aug 21, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning	Aug 1, 2014	ClassificationFeature Engineering	—Unverified
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter	Aug 1, 2014	Feature EngineeringSentiment Analysis	—Unverified
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities	Jun 1, 2018	Interpretable Machine LearningSentence Classification	—Unverified
Code Failure Prediction and Pattern Extraction using LSTM Networks	Dec 13, 2018	Bayesian OptimizationSentence	—Unverified
Code-Mixed Sentiment Analysis Using Machine Learning and Neural Network Approaches	Aug 9, 2018	BIG-bench Machine LearningSentence	—Unverified
Codeswitching Detection via Lexical Features in Conditional Random Fields	Nov 1, 2016	Automatic Speech Recognition (ASR)Sentiment Analysis	—Unverified
CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text	Jun 1, 2013	Sentiment Analysis	—Unverified
Coherency Improved Explainable Recommendation via Large Language Model	Feb 21, 2025	Explainable RecommendationExplanation Generation	—Unverified
Collecting and Evaluating Lexical Polarity with A Game With a Purpose	Sep 1, 2015	Sentiment Analysis	—Unverified
Collecting Code-Switched Data from Social Media	May 1, 2018	Language IdentificationLanguage Modeling	—Unverified
Collective Opinion Target Extraction in Chinese Microblogs	Oct 1, 2013	Dependency ParsingSentiment Analysis	—Unverified
Collective Sentiment Classification Based on User Leniency and Product Popularity	Nov 1, 2013	ClassificationGeneral Classification	—Unverified
Collocation Polarity Disambiguation Using Web-based Pseudo Contexts	Jul 1, 2012	Question AnsweringSentiment Analysis	—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	T5-11B	Accuracy	97.5	—	Unverified
3	MT-DNN-SMART	Accuracy	97.5	—	Unverified
4	T5-3B	Accuracy	97.4	—	Unverified
5	MUPPET Roberta Large	Accuracy	97.4	—	Unverified
6	StructBERTRoBERTa ensemble	Accuracy	97.1	—	Unverified
7	ALBERT	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