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 1776–1800 of 5630 papers

Title	Date	Tasks	Status	Hype
Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation	Dec 12, 2021	Discourse ParsingSentence	—Unverified	0
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces	Dec 12, 2021	AttributeSentiment Analysis	—Unverified	0
Sentiment Analysis on Brazilian Portuguese User Reviews	Dec 10, 2021	Document EmbeddingSentiment Analysis	—Unverified	0
Opinion Extraction as A Structured Sentiment Analysis using Transformers	Dec 9, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified	0
Zero-shot hashtag segmentation for multilingual sentiment analysis	Dec 6, 2021	Feature EngineeringHate Speech Detection	CodeCode Available	1
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks	Dec 6, 2021	Continual LearningSentiment Analysis	—Unverified	0
DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers	Dec 5, 2021	Language ModelingLanguage Modelling	CodeCode Available	0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks	Dec 5, 2021	ClassificationContinual Learning	—Unverified	0
Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification	Dec 5, 2021	EEGElectroencephalogram (EEG)	CodeCode Available	1
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning	Dec 5, 2021	Continual LearningLanguage Modeling	—Unverified	0
Exploratory Data Analysis of Urdu Poetry	Dec 3, 2021	DiversitySentiment Analysis	—Unverified	0
Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning	Dec 2, 2021	Contrastive LearningSentiment Analysis	—Unverified	0
ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors	Dec 2, 2021	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified	0
The Validity of Lexicon-based Sentiment Analysis in Interdisciplinary Research	Dec 1, 2021	Emotion RecognitionSentiment Analysis	—Unverified	0
IndicFed: A Federated Approach for Sentiment Analysis in Indic Languages	Dec 1, 2021	Federated LearningPrivacy Preserving	—Unverified	0
Sentiment Analysis For Bengali Using Transformer Based Models	Dec 1, 2021	Sentiment AnalysisSentiment Classification	—Unverified	0
A Pretrained YouTuber Embeddings for Improving Sentiment Classification of YouTube Comments	Dec 1, 2021	Sentiment AnalysisSentiment Classification	—Unverified	0
Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts	Dec 1, 2021	Binary ClassificationSentiment Analysis	—Unverified	0
Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis	Dec 1, 2021	Arabic Sentiment AnalysisDeep Learning	—Unverified	0
Generalizable Multi-linear Attention Network	Dec 1, 2021	Multimodal Sentiment AnalysisRetrieval	—Unverified	0
Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data	Nov 30, 2021	Graph AttentionSentiment Analysis	CodeCode Available	0
Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis	Nov 29, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat	Nov 29, 2021	Language ModelingLanguage Modelling	—Unverified	0
FastTrees: Parallel Latent Tree-Induction for Faster Sequence Encoding	Nov 28, 2021	Language ModelingLanguage Modelling	CodeCode Available	0
Topic Driven Adaptive Network for Cross-Domain Sentiment Classification	Nov 28, 2021	ClassificationDomain Adaptation	—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	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