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 3726–3750 of 5630 papers

Title	Date	Tasks	Status
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection	May 30, 2018	Opinion MiningSarcasm Detection	CodeCode Available
How Important Is a Neuron?	May 30, 2018	feature selectionSentiment Analysis	CodeCode Available
Convolutional neural network compression for natural language processing	May 28, 2018	Neural Network CompressionQuantization	—Unverified
Multimodal Sentiment Analysis To Explore the Structure of Emotions	May 25, 2018	Multimodal Sentiment AnalysisSentence	CodeCode Available
A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter	May 25, 2018	Decision MakingSentiment Analysis	—Unverified
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms	May 24, 2018	Document ClassificationGeneral Classification	CodeCode Available
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages	May 23, 2018	Cross-Lingual Sentiment ClassificationMachine Translation	CodeCode Available
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach	May 22, 2018	Feature EngineeringGeneral Classification	—Unverified
Aff2Vec: Affect--Enriched Distributional Word Representations	May 21, 2018	Natural Language UnderstandingSentiment Analysis	—Unverified
Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation	May 21, 2018	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Model Aggregation via Good-Enough Model Spaces	May 20, 2018	Distributed Optimizationmodel	—Unverified
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis	May 20, 2018	Graph EmbeddingKnowledge Graph Embedding	—Unverified
Diverse Few-Shot Text Classification with Multiple Metrics	May 19, 2018	ClassificationFew-Shot Learning	CodeCode Available
Aspect Based Sentiment Analysis with Gated Convolutional Networks	May 18, 2018	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Improved Sentence Modeling using Suffix Bidirectional LSTM	May 18, 2018	ClassificationGeneral Classification	—Unverified
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training	May 16, 2018	Domain AdaptationLanguage Identification	CodeCode Available
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors	May 14, 2018	Document ClassificationDomain Adaptation	CodeCode Available
Backpropagating through Structured Argmax using a SPIGOT	May 12, 2018	Dependency ParsingReinforcement Learning	CodeCode Available
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems	May 11, 2018	Sentiment AnalysisVocal Bursts Valence Prediction	—Unverified
Domain Adapted Word Embeddings for Improved Sentiment Classification	May 11, 2018	ClassificationGeneral Classification	CodeCode Available
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction	May 11, 2018	Aspect-Based Sentiment Analysis (ABSA)Aspect Extraction	CodeCode Available
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings	May 10, 2018	Data AugmentationGeneral Classification	—Unverified
Joint Embedding of Words and Labels for Text Classification	May 10, 2018	ClassificationGeneral Classification	CodeCode Available
Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network (RNN)	May 8, 2018	ClassificationGeneral Classification	CodeCode Available
Sentence-State LSTM for Text Representation	May 7, 2018	General ClassificationNamed Entity Recognition (NER)	CodeCode Available

Show:10 25 50

← PrevPage 150 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