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 4476–4500 of 5630 papers

Title	Date	Tasks	Status
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning	Apr 12, 2017	Sentiment AnalysisTransfer Learning	—Unverified
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks	Dec 25, 2024	ClassificationInformation Retrieval	—Unverified
Research on Annotation Rules and Recognition Algorithm Based on Phrase Window	Jul 7, 2020	Dependency ParsingSentence	—Unverified
Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis	Mar 13, 2024	Deep LearningSentiment Analysis	—Unverified
Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents	Dec 10, 2024	Decision MakingLanguage Modeling	—Unverified
Reserating the awesometastic: An automatic extension of the WordNet taxonomy for novel terms	May 1, 2015	Information RetrievalLEMMA	—Unverified
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs	Oct 1, 2013	Active LearningGeneral Classification	—Unverified
Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts	May 1, 2014	ClassificationGeneral Classification	—Unverified
Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu	Dec 1, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction	May 1, 2018	Sentiment Analysis	—Unverified
Resources and Experiments on Sentiment Classification for Georgian	Jun 1, 2022	ClassificationSentiment Analysis	—Unverified
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data	Oct 1, 2018	Semantic Textual SimilaritySentiment Analysis	—Unverified
Response Construct Tagging: NLP-Aided Assessment for Engineering Education	Jul 1, 2022	Sentiment AnalysisSentiment Classification	—Unverified
ResTS : Syst\`eme de R\'esum\'e Automatique des Textes d'Opinions bas\'e sur Twitter et SentiWordNet (System of Customer Review Summarization using Twitter and SentiWordNet) [in French]	Jun 1, 2012	Decision MakingOpinion Mining	—Unverified
Rethinking Annotation: Can Language Learners Contribute?	Oct 13, 2022	Machine Reading Comprehensionnamed-entity-recognition	—Unverified
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning	Mar 12, 2024	Aspect Sentiment Triplet ExtractionContrastive Learning	—Unverified
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture	May 5, 2025	Emotion ClassificationFeature Engineering	—Unverified
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies	Mar 29, 2021	Language ModelingLanguage Modelling	—Unverified
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning	Oct 29, 2023	Contrastive LearningFew-Shot Learning	—Unverified
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities	May 1, 2018	Named Entity Recognition (NER)Question Answering	—Unverified
RETUYT in TASS 2017: Sentiment Analysis for Spanish Tweets using SVM and CNN	Oct 17, 2017	Sentiment AnalysisWord Embeddings	—Unverified
Re-tweeting from a linguistic perspective	Jun 1, 2012	Sentiment Analysis	—Unverified
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics	Jun 25, 2019	General ClassificationSentiment Analysis	—Unverified
Review-Level Sentiment Classification with Sentence-Level Polarity Correction	Nov 7, 2015	BIG-bench Machine LearningClassification	—Unverified
Review Mining for Feature Based Opinion Summarization and Visualization	Apr 13, 2015	MarketingOpinion Mining	—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