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 3426–3450 of 5630 papers

Title	Date	Tasks	Status
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings	Oct 1, 2018	General ClassificationSentiment Analysis	—Unverified
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning	Oct 1, 2018	ClassificationEmotion Classification	CodeCode Available
Sentiment analysis under temporal shift	Oct 1, 2018	feature selectionSentiment Analysis	—Unverified
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation	Oct 1, 2018	Sentiment Analysis	—Unverified
Sentiment Expression Boundaries in Sentiment Polarity Classification	Oct 1, 2018	Aspect-Based Sentiment Analysis (ABSA)Classification	—Unverified
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification	Oct 1, 2018	General ClassificationSentiment Analysis	—Unverified
Learning representations for sentiment classification using Multi-task framework	Oct 1, 2018	ClassificationGeneral Classification	—Unverified
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification	Oct 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Saying no but meaning yes: negation and sentiment analysis in Basque	Oct 1, 2018	NegationSentiment Analysis	—Unverified
Disney at IEST 2018: Predicting Emotions using an Ensemble	Oct 1, 2018	Emotion ClassificationLanguage Modeling	—Unverified
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings	Oct 1, 2018	Document ClassificationGeneral Classification	—Unverified
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification	Oct 1, 2018	Emotion ClassificationEmotion Recognition	CodeCode Available
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings	Oct 1, 2018	General ClassificationSentiment Analysis	—Unverified
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network	Oct 1, 2018	Sentiment AnalysisWord Embeddings	—Unverified
The Role of Emotions in Native Language Identification	Oct 1, 2018	Deception DetectionLanguage Identification	—Unverified
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums	Oct 1, 2018	BIG-bench Machine LearningGeneral Classification	—Unverified
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition	Oct 1, 2018	Language ModelingLanguage Modelling	—Unverified
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection	Oct 1, 2018	Emotion RecognitionGeneral Classification	—Unverified
Aspect Based Sentiment Analysis into the Wild	Oct 1, 2018	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Arabizi sentiment analysis based on transliteration and automatic corpus annotation	Oct 1, 2018	Opinion MiningSentiment Analysis	—Unverified
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification	Oct 1, 2018	Emotion ClassificationGeneral Classification	—Unverified
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing	Oct 1, 2018	Emotion RecognitionSentence	CodeCode Available
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions	Oct 1, 2018	Sentiment AnalysisTopic Classification	—Unverified
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data	Oct 1, 2018	Semantic Textual SimilaritySentiment Analysis	—Unverified
Sequence Classification with Human Attention	Oct 1, 2018	Abusive LanguageClassification	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