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 1651–1675 of 5630 papers

Title	Date	Tasks	Status
Distilling BERT for low complexity network training	May 13, 2021	Sentiment AnalysisSST-2	—Unverified
Automatic Labelling of Topic Models Learned from Twitter by Summarisation	Jun 1, 2014	Sentiment AnalysisTopic Models	—Unverified
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh	Jan 24, 2021	ArticlesSentiment Analysis	—Unverified
An end-to-end Neural Network Framework for Text Clustering	Mar 22, 2019	ClusteringRepresentation Learning	—Unverified
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis	Aug 1, 2014	Sentiment Analysis	—Unverified
An Ensemble Approach to Question Classification: Integrating Electra Transformer, GloVe, and LSTM	Aug 13, 2023	ClassificationMachine Translation	—Unverified
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records	Apr 5, 2019	BIG-bench Machine LearningDecision Making	—Unverified
Distinguishing Common and Proper Nouns	Jun 1, 2013	Machine TranslationSentiment Analysis	—Unverified
Distinguishing Literal and Non-Literal Usage of German Particle Verbs	Jun 1, 2016	General ClassificationLanguage Identification	—Unverified
Distributed Deep Learning Using Volunteer Computing-Like Paradigm	Mar 16, 2021	Deep Learningimage-classification	—Unverified
Distributed Real-Time Sentiment Analysis for Big Data Social Streams	Dec 27, 2016	Distributed ComputingSentiment Analysis	—Unverified
Distributed Representations for Unsupervised Semantic Role Labeling	Sep 1, 2015	Language ModellingMachine Translation	—Unverified
COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines	Aug 1, 2017	PositionSentiment Analysis	—Unverified
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation	Apr 14, 2021	Sentiment AnalysisText Classification	—Unverified
Automatic Sarcasm Detection: A Survey	Feb 10, 2016	Sarcasm DetectionSentiment Analysis	—Unverified
Distribution of Emotional Reactions to News Articles in Twitter	May 1, 2018	ArticlesSentiment Analysis	—Unverified
A Societal Sentiment Analysis: Predicting the Values and Ethics of Individuals by Analysing Social Media Content	Apr 1, 2017	EthicsGeneral Classification	—Unverified
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning	Jul 1, 2018	Deep LearningMachine Translation	—Unverified
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification	Nov 1, 2020	General ClassificationMultiple Instance Learning	—Unverified
Automatic Triage of Mental Health Forum Posts	Jun 1, 2016	Sentiment AnalysisText Classification	—Unverified
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes	Dec 1, 2020	AllMulti-Task Learning	—Unverified
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	—Unverified
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification	Jun 1, 2014	ClassificationGeneral Classification	—Unverified
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil	Apr 1, 2021	Language IdentificationLanguage Modeling	—Unverified
As Long as You Name My Name Right: Social Circles and Social Sentiment in the Hollywood Hearings	Jun 1, 2014	Sentiment Analysis	—Unverified

Show:10 25 50

← PrevPage 67 of 226Next →

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