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 3801–3825 of 5630 papers

Title	Date	Tasks	Status
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only	Mar 14, 2019	Aspect ExtractionDescriptive	—Unverified
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects	Jun 27, 2021	Multimodal Sentiment AnalysisRecommendation Systems	—Unverified
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis	Apr 28, 2024	Language ModelingLanguage Modelling	—Unverified
Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets	Jan 25, 2021	Domain AdaptationSentiment Analysis	—Unverified
Transfer Learning for Sequences via Learning to Collocate	Feb 25, 2019	NERPOS	—Unverified
Transfer Learning with Dynamic Distribution Adaptation	Sep 17, 2019	Domain Adaptationimage-classification	—Unverified
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya	Jun 13, 2020	Language ModelingLanguage Modelling	—Unverified
Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis	Jan 16, 2024	Sentiment Analysis	—Unverified
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis	Nov 1, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text	Aug 1, 2021	Chinese Sentiment AnalysisPosition	—Unverified
Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach	Oct 25, 2020	Sentiment Analysis	—Unverified
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model	Oct 19, 2024	Language ModelingLanguage Modelling	—Unverified
Translating Dialectal Arabic as Low Resource Language using Word Embedding	Sep 1, 2017	Machine TranslationSentiment Analysis	—Unverified
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis	Sep 7, 2020	Machine TranslationMultimodal Sentiment Analysis	—Unverified
TransRev: Modeling Reviews as Translations from Users to Items	Jan 30, 2018	Product RecommendationRecommendation Systems	—Unverified
TRBLLmaker -- Transformer Reads Between Lyrics Lines maker	Dec 9, 2022	DecoderSentiment Analysis	—Unverified
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers	Aug 1, 2017	BIG-bench Machine LearningNamed Entity Recognition (NER)	—Unverified
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media	Feb 24, 2014	ClusteringGraph Clustering	—Unverified
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth	Jan 16, 2016	Emotion RecognitionSentiment Analysis	—Unverified
Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification	Apr 16, 2025	Large Language ModelSentiment Analysis	—Unverified
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness	May 9, 2024	FairnessSentiment Analysis	—Unverified
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space	Apr 13, 2024	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified
Try This Instead: Personalized and Interpretable Substitute Recommendation	May 19, 2020	AttributeCollaborative Filtering	—Unverified
TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis	Aug 1, 2017	General ClassificationSentiment Analysis	—Unverified
TUGAS: Exploiting unlabelled data for Twitter sentiment analysis	Aug 1, 2014	Sentiment AnalysisTwitter Sentiment 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	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