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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4676–4700 of 5630 papers

Title	Date	Tasks	Status
Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features	Jan 3, 2016	Document SummarizationExtractive Summarization	—Unverified
Sentiment/Subjectivity Analysis Survey for Languages other than English	Jan 1, 2016	Arabic Sentiment AnalysisSentiment Analysis	—Unverified
High, Medium or Low? Detecting Intensity Variation Among polar synonyms in WordNet	Jan 1, 2016	Sentiment Analysis	—Unverified
A Language-independent Model for Introducing a New Semantic Relation Between Adjectives and Nouns in a WordNet	Jan 1, 2016	AttributeRelation	—Unverified
Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews	Dec 27, 2015	General ClassificationSentiment Analysis	CodeCode Available
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks	Dec 16, 2015	Bayesian InferenceDeep Learning	CodeCode Available
Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics	Dec 15, 2015	Sentiment Analysis	—Unverified
Semisupervised Autoencoder for Sentiment Analysis	Dec 14, 2015	Sentiment Analysis	—Unverified
Words are not Equal: Graded Weighting Model for building Composite Document Vectors	Dec 11, 2015	Sentiment AnalysisSentiment Classification	—Unverified
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods	Dec 6, 2015	ArticlesSentence	—Unverified
Approaches for Sentiment Analysis on Twitter: A State-of-Art study	Dec 3, 2015	Opinion MiningRetrieval	—Unverified
Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs	Dec 2, 2015	BenchmarkingSentiment Analysis	CodeCode Available
Using Skipgrams, Bigrams, and Part of Speech Features for Sentiment Classification of Twitter Messages	Dec 1, 2015	General ClassificationOpinion Mining	—Unverified
Simultaneous Feature Selection and Parameter Optimization Using Multi-objective Optimization for Sentiment Analysis	Dec 1, 2015	feature selectionOpinion Mining	—Unverified
More Efficient Topic Modelling Through a Noun Only Approach	Dec 1, 2015	Intrusion DetectionSentiment Analysis	—Unverified
Learning Using 1-Local Membership Queries	Dec 1, 2015	Machine TranslationSentence	—Unverified
Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features	Dec 1, 2015	Machine TranslationNamed Entity Recognition (NER)	—Unverified
Sentence Boundary Detection for Social Media Text	Dec 1, 2015	Boundary DetectionDocument Summarization	—Unverified
Domain Sentiment Matters: A Two Stage Sentiment Analyzer	Dec 1, 2015	Sentiment AnalysisVocal Bursts Valence Prediction	—Unverified
Ruchi: Rating Individual Food Items in Restaurant Reviews	Dec 1, 2015	Information RetrievalNamed Entity Recognition (NER)	—Unverified
Aspect-based Opinion Summarization with Convolutional Neural Networks	Nov 30, 2015	Aspect ExtractionClassification	—Unverified
Sentiment Analysis on YouTube: A Brief Survey	Nov 30, 2015	Opinion MiningSentiment Analysis	—Unverified
Machine Learning Sentiment Prediction based on Hybrid Document Representation	Nov 29, 2015	BIG-bench Machine LearningGeneral Classification	—Unverified
A C-LSTM Neural Network for Text Classification	Nov 27, 2015	General ClassificationSentence	CodeCode Available
Category Enhanced Word Embedding	Nov 27, 2015	General ClassificationRepresentation Learning	—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