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 4801–4825 of 5630 papers

Title	Date	Tasks	Status
Enabling Complex Wikipedia Queries - Technical Report	Aug 13, 2015	Information RetrievalRecommendation Systems	—Unverified
On Gobbledygook and Mood of the Philippine Senate: An Exploratory Study on the Readability and Sentiment of Selected Philippine Senators' Microposts	Aug 6, 2015	Sentiment Analysis	—Unverified
Class Vectors: Embedding representation of Document Classes	Aug 2, 2015	General ClassificationNatural Language Understanding	—Unverified
Document Embedding with Paragraph Vectors	Jul 29, 2015	Document EmbeddingSentiment Analysis	CodeCode Available
Target-dependent twitter sentiment classification with rich automatic features	Jul 25, 2015	ClassificationSentiment Analysis	—Unverified
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination	Jul 3, 2015	BIG-bench Machine LearningClassification	—Unverified
Chinese Microblogs Sentiment Classification using Maximum Entropy	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
CT-SPA: Text sentiment polarity prediction model using semi-automatically expanded sentiment lexicon	Jul 1, 2015	Opinion MiningSentiment Analysis	—Unverified
Deep Learning Models for Sentiment Analysis in Arabic	Jul 1, 2015	Deep LearningOpinion Mining	—Unverified
Analyzing Sentiment in Classical Chinese Poetry	Jul 1, 2015	Sentiment Analysis	—Unverified
NEUDM: A System for Topic-Based Message Polarity Classification	Jul 1, 2015	ClassificationDependency Parsing	—Unverified
Improving Twitter Named Entity Recognition using Word Representations	Jul 1, 2015	named-entity-recognitionNamed Entity Recognition	—Unverified
Rule-Based Weibo Messages Sentiment Polarity Classification towards Given Topics	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
Towards POS Tagging for Arabic Tweets	Jul 1, 2015	Machine TranslationPart-Of-Speech Tagging	—Unverified
Sequential Annotation and Chunking of Chinese Discourse Structure	Jul 1, 2015	ChunkingMachine Translation	—Unverified
Overview of Topic-based Chinese Message Polarity Classification in SIGHAN 2015	Jul 1, 2015	General ClassificationInformation Retrieval	—Unverified
NDMSCS: A Topic-Based Chinese Microblog Polarity Classification System	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
NCSU\_SAS\_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization	Jul 1, 2015	Named Entity Recognition (NER)Part-Of-Speech Tagging	—Unverified
Topic-Based Chinese Message Sentiment Analysis: A Multilayered Analysis System	Jul 1, 2015	Emotion RecognitionSentiment Analysis	—Unverified
Topic-Based Chinese Message Polarity Classification System at SIGHAN8-Task2	Jul 1, 2015	ClassificationGeneral Classification	—Unverified
A Joint Model for Chinese Microblog Sentiment Analysis	Jul 1, 2015	Feature Engineeringmodel	—Unverified
The Web as an Implicit Training Set: Application to Noun Compounds Syntax and Semantics	Jul 1, 2015	Community Question AnsweringMachine Translation	—Unverified
ACBiMA: Advanced Chinese Bi-Character Word Morphological Analyzer	Jul 1, 2015	Morphological AnalysisSentiment Analysis	—Unverified
A Linked Data Model for Multimodal Sentiment and Emotion Analysis	Jul 1, 2015	Emotion RecognitionOpinion Mining	—Unverified
Bekli:A Simple Approach to Twitter Text Normalization.	Jul 1, 2015	EpidemiologySentiment Analysis	—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