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 3226–3250 of 5630 papers

Title	Date	Tasks	Status
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification	Jun 1, 2019	General ClassificationSentiment Analysis	—Unverified
Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features	Jun 1, 2019	Bias DetectionSentiment Analysis	—Unverified
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis	Jun 1, 2019	Sentiment Analysis	—Unverified
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision	Jun 1, 2019	Sentiment AnalysisTranslation	—Unverified
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning	Jun 1, 2019	Sentiment Analysis	—Unverified
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings	Jun 1, 2019	Multi-class ClassificationMulti-Task Learning	—Unverified
Adversarial Category Alignment Network for Cross-domain Sentiment Classification	Jun 1, 2019	ClassificationGeneral Classification	—Unverified
Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector	Jun 1, 2019	ArticlesSentiment Analysis	—Unverified
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach	Jun 1, 2019	NegationReinforcement Learning	—Unverified
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling	Jun 1, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining	Jun 1, 2019	Sentiment AnalysisSuggestion mining	—Unverified
YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation	Jun 1, 2019	Ensemble LearningSentiment Analysis	—Unverified
Incorporating Emoji Descriptions Improves Tweet Classification	Jun 1, 2019	ClassificationGeneral Classification	—Unverified
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning	Jun 1, 2019	Sentiment Analysis	—Unverified
UAIC at SemEval-2019 Task 3: Extracting Much from Little	Jun 1, 2019	Sentiment Analysis	—Unverified
Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector	Jun 1, 2019	Articlesfeature selection	—Unverified
Examining Structure of Word Embeddings with PCA	May 31, 2019	DecoderMachine Translation	—Unverified
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English	May 31, 2019	Emotion ClassificationEmotion Recognition	—Unverified
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning	May 31, 2019	Document ClassificationEnsemble Learning	CodeCode Available
Investigating an Effective Character-level Embedding in Korean Sentence Classification	May 31, 2019	ClassificationGeneral Classification	CodeCode Available
LANGUAGE MODEL EMBEDDINGS IMPROVE SENTIMENT ANALYSIS IN RUSSIAN	May 29, 2019	ArticlesLanguage Modeling	CodeCode Available
Differential Privacy Has Disparate Impact on Model Accuracy	May 28, 2019	Gender ClassificationGeneral Classification	CodeCode Available
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization	May 28, 2019	General ClassificationMachine Translation	CodeCode Available
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction	May 28, 2019	modelPrediction	—Unverified
Combating Adversarial Misspellings with Robust Word Recognition	May 27, 2019	Sentiment Analysis	CodeCode Available

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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