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 3326–3350 of 5630 papers

Title	Date	Tasks	Status
Huntsville, hospitals, and hockey teams: Names can reveal your location	Sep 1, 2017	Knowledge Base PopulationRecommendation Systems	—Unverified
Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features	Jan 3, 2016	Document SummarizationExtractive Summarization	—Unverified
Hybrid approach to detecting symptoms of depression in social media entries	Jun 19, 2021	DiagnosticSentiment Analysis	—Unverified
Hybrid Attention based Multimodal Network for Spoken Language Classification	Aug 1, 2018	ClassificationDeep Learning	—Unverified
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis	Sep 4, 2021	Contrastive LearningMultimodal Sentiment Analysis	—Unverified
Hybrid Deep Belief Networks for Semi-supervised Sentiment Classification	Aug 1, 2014	ClassificationGeneral Classification	—Unverified
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis	Mar 27, 2025	Emotion RecognitionSentiment Analysis	—Unverified
Hybrid Improved Document-level Embedding (HIDE)	Jun 1, 2020	Sentiment AnalysisWord Embeddings	—Unverified
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification	Oct 1, 2015	Document ClassificationDomain Adaptation	—Unverified
Hybrid Models for Lexical Acquisition of Correlated Styles	Oct 1, 2013	Sentiment AnalysisText Generation	—Unverified
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates	Oct 1, 2018	Natural Language InferenceSentiment Analysis	—Unverified
Hybrid Quantum-Classical Machine Learning for Sentiment Analysis	Oct 8, 2023	Dimensionality ReductionSentiment Analysis	—Unverified
Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining	Jun 1, 2019	Sentiment AnalysisSuggestion mining	—Unverified
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification	Jan 31, 2020	ClassificationGeneral Classification	—Unverified
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter	Jun 1, 2016	Emotion RecognitionGeneral Classification	—Unverified
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems	Nov 12, 2024	Adversarial TextSentiment Analysis	—Unverified
"I ain't tellin' white folks nuthin": A quantitative exploration of the race-related problem of candour in the WPA slave narratives	May 1, 2018	Sentiment Analysis	—Unverified
IBA-Sys at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News	Aug 1, 2017	regressionSentiment Analysis	—Unverified
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic	Apr 1, 2021	Sarcasm DetectionSentiment Analysis	—Unverified
Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models	Nov 25, 2021	FairnessSentiment Analysis	—Unverified
Identification of emotions on Twitter during the 2022 electoral process in Colombia	Jul 9, 2024	Few-Shot LearningOpinion Mining	—Unverified
Identification of the Breach of Short-term Rental Regulations in Irish Rent Pressure Zones	Nov 29, 2022	Sentiment Analysis	—Unverified
Identifying Adversarial Attacks on Text Classifiers	Jan 21, 2022	Abuse DetectionAdversarial Text	—Unverified
Identifying Emotion Labels from Psychiatric Social Texts Using Independent Component Analysis	Aug 1, 2014	Sentiment Analysis	—Unverified
Identifying High-Impact Sub-Structures for Convolution Kernels in Document-level Sentiment Classification	Jul 1, 2012	General ClassificationSentiment 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