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 4851–4900 of 5630 papers

Title	Date	Tasks	Status
NEUDM: A System for Topic-Based Message Polarity Classification	Jul 1, 2015	ClassificationDependency Parsing	—Unverified
Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis	Jul 1, 2015	Language ModellingMachine Translation	—Unverified
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation	Jul 1, 2015	General ClassificationSentiment Analysis	—Unverified
Aspect-Level Cross-lingual Sentiment Classification with Constrained SMT	Jul 1, 2015	ClassificationCross-Lingual Sentiment Classification	—Unverified
KeLP: a Kernel-based Learning Platform for Natural Language Processing	Jul 1, 2015	Boundary DetectionFeature Engineering	—Unverified
A system for fine-grained aspect-based sentiment analysis of Chinese	Jul 1, 2015	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces	Jul 1, 2015	Part-Of-Speech TaggingSentiment Analysis	—Unverified
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
Towards a Contextual Pragmatic Model to Detect Irony in Tweets	Jul 1, 2015	Sentiment Analysis	—Unverified
Chinese Microblogs Sentiment Classification using Maximum Entropy	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
Sequential Annotation and Chunking of Chinese Discourse Structure	Jul 1, 2015	ChunkingMachine Translation	—Unverified
Automatic disambiguation of English puns	Jul 1, 2015	Sentiment AnalysisText Generation	—Unverified
Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction	Jul 1, 2015	Sentiment AnalysisStock Market Prediction	—Unverified
Entity Hierarchy Embedding	Jul 1, 2015	Entity LinkingInformation Retrieval	—Unverified
An combined sentiment classification system for SIGHAN-8	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
Learning to Adapt Credible Knowledge in Cross-lingual Sentiment Analysis	Jul 1, 2015	Machine TranslationSentiment Analysis	—Unverified
A Shallow Discourse Parsing System Based On Maximum Entropy Model	Jul 1, 2015	Discourse ParsingOpinion Mining	—Unverified
Analyzing Sentiment in Classical Chinese Poetry	Jul 1, 2015	Sentiment Analysis	—Unverified
Classification of Research Citations (CRC)	Jun 30, 2015	ClassificationGeneral Classification	—Unverified
Occam's Gates	Jun 27, 2015	General ClassificationQuestion Answering	—Unverified
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing	Jun 24, 2015	General ClassificationPart-Of-Speech Tagging	CodeCode Available
Entity-Specific Sentiment Classification of Yahoo News Comments	Jun 11, 2015	ArticlesClassification	—Unverified
On-the-Job Learning with Bayesian Decision Theory	Jun 10, 2015	General Classificationimage-classification	CodeCode Available
Idioms-Proverbs Lexicon for Modern Standard Arabic and Colloquial Sentiment Analysis	Jun 5, 2015	Opinion MiningSentence	—Unverified
Do Multi-Sense Embeddings Improve Natural Language Understanding?	Jun 2, 2015	named-entity-recognitionNamed Entity Recognition	—Unverified
Video (GIF) Sentiment Analysis using Large-Scale Mid-Level Ontology	Jun 2, 2015	Sentiment Analysis	—Unverified
UDLAP: Sentiment Analysis Using a Graph-Based Representation	Jun 1, 2015	Sentiment Analysis	—Unverified
KLUEless: Polarity Classification and Association	Jun 1, 2015	ClassificationGeneral Classification	—Unverified
WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition	Jun 1, 2015	Sentiment AnalysisWord Embeddings	—Unverified
UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter	Jun 1, 2015	Sentiment Analysis	—Unverified
Webis: An Ensemble for Twitter Sentiment Detection	Jun 1, 2015	Sentiment AnalysisText Classification	—Unverified
DeepNL: a Deep Learning NLP pipeline	Jun 1, 2015	Constituency ParsingDeep Learning	—Unverified
Rule-based Coreference Resolution in German Historic Novels	Jun 1, 2015	coreference-resolutionCoreference Resolution	—Unverified
CAN\'EPHORE : un corpus fran pour la fouille d'opinion cibl\'ee	Jun 1, 2015	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter	Jun 1, 2015	ClassificationGeneral Classification	—Unverified
Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets	Jun 1, 2015	Sentiment Analysis	—Unverified
SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification	Jun 1, 2015	Opinion MiningSentiment Analysis	—Unverified
SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression	Jun 1, 2015	Negation Detectionregression	—Unverified
SWATCS65: Sentiment Classification Using an Ensemble of Class Projects	Jun 1, 2015	ClassificationGeneral Classification	—Unverified
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification	Jun 1, 2015	ClassificationGeneral Classification	—Unverified
Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment	Jun 1, 2015	General ClassificationSentiment Analysis	—Unverified
SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis	Jun 1, 2015	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis	Jun 1, 2015	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews	Jun 1, 2015	Aspect-Based Sentiment Analysis (ABSA)Opinion Mining	—Unverified
GTI: An Unsupervised Approach for Sentiment Analysis in Twitter	Jun 1, 2015	Dependency ParsingSentiment Analysis	—Unverified
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces	Jun 1, 2015	Feature EngineeringSentiment Analysis	—Unverified
KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter	Jun 1, 2015	Sentiment Analysis	—Unverified
SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning	Jun 1, 2015	Decision MakingSentiment Analysis	—Unverified
Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis	Jun 1, 2015	regressionSentiment Analysis	—Unverified
Learning Structures of Negations from Flat Annotations	Jun 1, 2015	Information RetrievalNegation Detection	—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