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 4576–4600 of 5630 papers

Title	Date	Tasks	Status
Artificial Intelligence for the Internal Democracy of Political Parties	Apr 2, 2024	ManagementSentiment Analysis	—Unverified
Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models	Dec 15, 2019	FairnessSentiment Analysis	—Unverified
A Rule-Based Approach to Aspect Extraction from Product Reviews	Aug 1, 2014	Aspect ExtractionCommon Sense Reasoning	—Unverified
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset	Nov 1, 2020	Arabic Sentiment AnalysisSentiment Analysis	—Unverified
A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning	Jul 15, 2019	Sentiment Analysis	—Unverified
A Scalable, Lexicon Based Technique for Sentiment Analysis	Oct 8, 2014	Opinion MiningSentiment Analysis	—Unverified
ASDA : Analyseur Syntaxique du Dialecte Algérien dans un but d'analyse sémantique	Jul 26, 2017	Opinion MiningPOS	—Unverified
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences	Nov 12, 2022	Multimodal Sentiment AnalysisRepresentation Learning	—Unverified
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification	Nov 14, 2018	Abstractive Text SummarizationClassification	—Unverified
A semantic-affective compositional approach for the affective labelling of adjective-noun and noun-noun pairs	Jun 1, 2016	Semantic Textual SimilaritySentiment Analysis	—Unverified
A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language Processing	Feb 5, 2023	NegationNegation Detection	—Unverified
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention	Jul 27, 2024	Fake News DetectionSelf-Learning	—Unverified
A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents	Jun 10, 2021	Classificationintent-classification	—Unverified
A Sentiment-aligned Topic Model for Product Aspect Rating Prediction	Oct 1, 2014	Opinion MiningSentiment Analysis	—Unverified
A Sentiment Analysis Approach to the Prediction of Market Volatility	Dec 10, 2020	ManagementPortfolio Optimization	—Unverified
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation	Aug 30, 2021	Sentiment Analysis	—Unverified
A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT	Jun 6, 2022	Decision MakingSentiment Analysis	—Unverified
A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter	May 25, 2018	Decision MakingSentiment Analysis	—Unverified
A Sentiment Analysis of Medical Text Based on Deep Learning	Apr 16, 2024	Deep LearningModel Selection	—Unverified
A Sentiment Analysis of Men’s and Women’s Speech in the BNC64	Nov 1, 2021	Sentiment Analysis	—Unverified
A Sentiment Analyzer for Hindi Using Hindi Senti Lexicon	Dec 1, 2014	Sentiment Analysis	—Unverified
A Sentiment Corpus for South African Under-Resourced Languages in a Multilingual Context	Jun 1, 2022	Sentiment Analysis	—Unverified
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences	Nov 15, 2017	ChunkingNatural Language Inference	—Unverified
A Shallow Discourse Parsing System Based On Maximum Entropy Model	Jul 1, 2015	Discourse ParsingOpinion Mining	—Unverified
A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis	Nov 1, 2020	SentenceSentiment 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