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 4701–4750 of 5630 papers

Title	Date	Tasks	Status
Towards Universal Paraphrastic Sentence Embeddings	Nov 25, 2015	General ClassificationSentence	—Unverified
Multimodal sparse representation learning and applications	Nov 19, 2015	ClassificationDenoising	—Unverified
Overcoming Language Variation in Sentiment Analysis with Social Attention	Nov 19, 2015	Sentiment Analysis	CodeCode Available
A System for Extracting Sentiment from Large-Scale Arabic Social Data	Nov 15, 2015	Arabic Sentiment AnalysisGeneral Classification	—Unverified
Sentiment Expression via Emoticons on Social Media	Nov 9, 2015	ClusteringSentiment Analysis	—Unverified
Review-Level Sentiment Classification with Sentence-Level Polarity Correction	Nov 7, 2015	BIG-bench Machine LearningClassification	—Unverified
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding	Nov 5, 2015	BIG-bench Machine LearningGeneral Classification	—Unverified
The Variational Fair Autoencoder	Nov 3, 2015	General ClassificationSentiment Analysis	CodeCode Available
7x1-PT: um Corpus extra\' do Twitter para An\'alise de Sentimentos em L\' Portuguesa (7x1-PT: a Corpus extracted from Twitter for Sentiment Analysis in Portuguese Language)	Nov 1, 2015	Sentiment Analysis	—Unverified
An Annotated Corpus for Sentiment Analysis in Political News	Nov 1, 2015	Sentiment Analysis	—Unverified
Um novo corpo e os seus desafios (A new corpus and the challenges it offers)	Nov 1, 2015	Question AnsweringSentiment Analysis	—Unverified
Anotando um Corpus de Not\' para a An\'alise de Sentimentos: um Relato de Experi\^encia (Annotating a corpus of News for Sentiment Analysis: An Experience Report)	Nov 1, 2015	Sentiment Analysis	—Unverified
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis	Oct 30, 2015	Sentiment AnalysisVocal Bursts Intensity Prediction	—Unverified
Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis	Oct 18, 2015	BIG-bench Machine LearningSentiment Analysis	—Unverified
Mapping Unseen Words to Task-Trained Embedding Spaces	Oct 8, 2015	Dependency ParsingSentiment Analysis	—Unverified
Predicting Sector Index Movement with Microblogging Public Mood Time Series on Social Issues	Oct 1, 2015	Sentiment AnalysisStock Prediction	—Unverified
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation	Oct 1, 2015	DenoisingGeneral Classification	—Unverified
Is Wikipedia Really Neutral? A Sentiment Perspective Study of War-related Wikipedia Articles since 1945	Oct 1, 2015	ArticlesSentiment Analysis	—Unverified
Thai Stock News Sentiment Classification using Wordpair Features	Oct 1, 2015	ClassificationGeneral Classification	—Unverified
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification	Oct 1, 2015	Document ClassificationDomain Adaptation	—Unverified
Toward a Corpus of Cantonese Verbal Comments and their Classification by Multi-dimensional Analysis	Oct 1, 2015	General ClassificationOpinion Mining	—Unverified
Polarity Classification of Short Product Reviews via Multiple Cluster-based SVM Classifiers	Oct 1, 2015	ClusteringGeneral Classification	—Unverified
Multi-aspects Rating Prediction Using Aspect Words and Sentences	Oct 1, 2015	General ClassificationPrediction	—Unverified
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features	Oct 1, 2015	Emotion ClassificationEmotion Recognition	—Unverified
Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms	Oct 1, 2015	Document ClassificationGeneral Classification	—Unverified
Enhancing Root Extractors Using Light Stemmers	Oct 1, 2015	Machine TranslationPart-Of-Speech Tagging	—Unverified
Sentiment Analyzer with Rich Features for Ironic and Sarcastic Tweets	Oct 1, 2015	Opinion MiningSentiment Analysis	—Unverified
Sentiment Uncertainty and Spam in Twitter Streams and Its Implications for General Purpose Realtime Sentiment Analysis	Sep 25, 2015	Sentiment AnalysisTwitter Sentiment Analysis	—Unverified
Sentiment of Emojis	Sep 25, 2015	Sentiment Analysis	—Unverified
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks	Sep 20, 2015	Sentiment Analysis	—Unverified
Building a Pilot Software Quality-in-Use Benchmark Dataset	Sep 18, 2015	SentenceSentiment Analysis	—Unverified
Twitter Sentiment Analysis	Sep 14, 2015	Sentiment AnalysisSentiment Classification	—Unverified
Improved Twitter Sentiment Prediction through Cluster-then-Predict Model	Sep 8, 2015	ClusteringGeneral Classification	—Unverified
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces	Sep 5, 2015	Domain AdaptationObject Recognition	—Unverified
Better Document-level Sentiment Analysis from RST Discourse Parsing	Sep 4, 2015	Discourse ParsingGeneral Classification	—Unverified
Some Theoretical Considerations in Off-the-Shelf Text Analysis Software	Sep 1, 2015	Deception DetectionSentiment Analysis	—Unverified
Collecting and Evaluating Lexical Polarity with A Game With a Purpose	Sep 1, 2015	Sentiment Analysis	—Unverified
A Two-level Classifier for Discriminating Similar Languages	Sep 1, 2015	Language IdentificationMachine Translation	—Unverified
POS Tagging for Arabic Tweets	Sep 1, 2015	Machine TranslationPart-Of-Speech Tagging	—Unverified
Optimising Agile Social Media Analysis	Sep 1, 2015	Opinion MiningSentiment Analysis	—Unverified
Sentiment Classification via a Response Recalibration Framework	Sep 1, 2015	ClassificationGeneral Classification	—Unverified
A Large Wordnet-based Sentiment Lexicon for Polish	Sep 1, 2015	ClusteringOpinion Mining	—Unverified
Using Combined Lexical Resources to Identify Hashtag Types	Sep 1, 2015	Sentiment Analysis	—Unverified
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization	Sep 1, 2015	SentenceSentiment Analysis	—Unverified
Sentiment Flow - A General Model of Web Review Argumentation	Sep 1, 2015	Sentiment Analysis	—Unverified
Verb-centered Sentiment Inference with Description Logics	Sep 1, 2015	Sentiment Analysis	—Unverified
Evaluation of Word Vector Representations by Subspace Alignment	Sep 1, 2015	ChunkingNamed Entity Recognition (NER)	CodeCode Available
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings	Sep 1, 2015	Feature EngineeringOpinion Mining	—Unverified
Fine-Grained Sentiment Analysis for Movie Reviews in Bulgarian	Sep 1, 2015	General ClassificationSentiment Analysis	—Unverified
PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis	Sep 1, 2015	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—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