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 5001–5025 of 5630 papers

Title	Date	Tasks	Status
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks	Dec 1, 2014	General ClassificationImage to text	CodeCode Available
LABR: A Large Scale Arabic Sentiment Analysis Benchmark	Nov 25, 2014	Arabic Sentiment AnalysisClassification	CodeCode Available
Visual Sentiment Prediction with Deep Convolutional Neural Networks	Nov 21, 2014	Object RecognitionPrediction	—Unverified
The Effect of Temporal-based Term Selection for Text Classification	Nov 1, 2014	ClassificationGeneral Classification	—Unverified
Sentiment Analysis based on User Tag for Traditional Chinese Medicine in Weibo	Oct 13, 2014	Cultural Vocal Bursts Intensity PredictionSentiment Analysis	—Unverified
Polarization Measurement of High Dimensional Social Media Messages With Support Vector Machine Algorithm Using Mapreduce	Oct 10, 2014	ClassificationGeneral Classification	—Unverified
A Scalable, Lexicon Based Technique for Sentiment Analysis	Oct 8, 2014	Opinion MiningSentiment Analysis	—Unverified
Corpora Preparation and Stopword List Generation for Arabic data in Social Network	Oct 5, 2014	feature selectionGeneral Classification	—Unverified
A Supervised Approach for Sentiment Analysis using Skipgrams	Oct 1, 2014	Sentiment Analysis	—Unverified
Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning	Oct 1, 2014	Cross-Lingual TransferDependency Parsing	—Unverified
Predicting Attrition Along the Way: The UIUC Model	Oct 1, 2014	modelSentiment Analysis	—Unverified
Foreign Words and the Automatic Processing of Arabic Social Media Text Written in Roman Script	Oct 1, 2014	Machine TranslationSentiment Analysis	—Unverified
A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining	Oct 1, 2014	Opinion MiningSentiment Analysis	—Unverified
Evaluating Distant Supervision for Subjectivity and Sentiment Analysis on Arabic Twitter Feeds	Oct 1, 2014	Sentiment Analysis	—Unverified
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency	Oct 1, 2014	ClusteringSentiment Analysis	—Unverified
Event Role Extraction using Domain-Relevant Word Representations	Oct 1, 2014	Machine TranslationNamed Entity Recognition (NER)	—Unverified
Sentiment Analysis on the People's Daily	Oct 1, 2014	Decision MakingSentiment Analysis	—Unverified
Abstractive Summarization of Product Reviews Using Discourse Structure	Oct 1, 2014	Abstractive Text SummarizationSentiment Analysis	—Unverified
The Inside-Outside Recursive Neural Network model for Dependency Parsing	Oct 1, 2014	Dependency ParsingLanguage Modelling	—Unverified
+/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference	Oct 1, 2014	Opinion MiningSentiment Analysis	—Unverified
Aligning context-based statistical models of language with brain activity during reading	Oct 1, 2014	Language ModellingMachine Translation	—Unverified
Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis	Oct 1, 2014	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
A Dependency Parser for Tweets	Oct 1, 2014	Dependency ParsingDomain Adaptation	—Unverified
Exploiting Social Relations and Sentiment for Stock Prediction	Oct 1, 2014	PredictionSentiment Analysis	—Unverified
A Neural Network for Factoid Question Answering over Paragraphs	Oct 1, 2014	Information RetrievalLanguage Modelling	—Unverified

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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