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 4401–4425 of 5630 papers

Title	Date	Tasks	Status	Hype
Convolutional Neural Network Language Models	Nov 1, 2016	Document ClassificationGeneral Classification	CodeCode Available	0
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network	Nov 1, 2016	ClusteringLearning Word Embeddings	—Unverified	0
Neural Sentiment Classification with User and Product Attention	Nov 1, 2016	ClassificationFeature Engineering	CodeCode Available	0
Context-Sensitive Lexicon Features for Neural Sentiment Analysis	Nov 1, 2016	Opinion MiningSentiment Analysis	CodeCode Available	0
Does `well-being' translate on Twitter?	Nov 1, 2016	Machine TranslationSentiment Analysis	—Unverified	0
Automatic Extraction of Implicit Interpretations from Modal Constructions	Nov 1, 2016	Machine TranslationNatural Language Inference	—Unverified	0
Robust Gram Embeddings	Nov 1, 2016	Sarcasm DetectionSentiment Analysis	CodeCode Available	0
Extracting Aspect Specific Opinion Expressions	Nov 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Opinion Mining	—Unverified	0
Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems	Nov 1, 2016	Dialogue ManagementEmotion Recognition	—Unverified	0
Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets	Nov 1, 2016	Opinion MiningSentiment Analysis	—Unverified	0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings	Nov 1, 2016	General ClassificationSentiment Analysis	—Unverified	0
Mining Social Media for Open Innovation in Transportation Systems	Oct 31, 2016	Sentiment Analysis	—Unverified	0
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier	Oct 31, 2016	Recommendation SystemsSentence	—Unverified	0
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements	Oct 28, 2016	Sentiment AnalysisStock Market Prediction	CodeCode Available	1
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks	Oct 27, 2016	Sarcasm DetectionSentence	CodeCode Available	0
Lexicon Integrated CNN Models with Attention for Sentiment Analysis	Oct 20, 2016	Sentiment AnalysisWord Embeddings	—Unverified	0
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification	Oct 17, 2016	ClassificationFeature Engineering	—Unverified	0
SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods	Oct 12, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Supervised Term Weighting Metrics for Sentiment Analysis in Short Text	Oct 10, 2016	General ClassificationInformation Retrieval	—Unverified	0
Correlation-Based Method for Sentiment Classification	Oct 10, 2016	ClassificationGeneral Classification	—Unverified	0
Neural Structural Correspondence Learning for Domain Adaptation	Oct 5, 2016	DenoisingDomain Adaptation	CodeCode Available	0
Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models	Oct 2, 2016	Sentiment Analysis	—Unverified	0
Sarcasm Detection in Chinese Using a Crowdsourced Corpus	Oct 1, 2016	Sarcasm DetectionSentiment Analysis	—Unverified	0
Computing Sentiment Scores of Verb Phrases for Vietnamese	Oct 1, 2016	Common Sense ReasoningMachine Translation	—Unverified	0
Event Based Emotion Classification for News Articles	Oct 1, 2016	ArticlesClassification	—Unverified	0

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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