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 901–925 of 5630 papers

Title	Date	Tasks	Status	Score
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis	Apr 4, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	5
Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews	Dec 27, 2015	General ClassificationSentiment Analysis	CodeCode Available	5
Document Embedding with Paragraph Vectors	Jul 29, 2015	Document EmbeddingSentiment Analysis	CodeCode Available	5
A Deep Relevance Model for Zero-Shot Document Filtering	Jul 1, 2018	Sentiment AnalysisText Classification	CodeCode Available	5
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings	Aug 1, 2018	General ClassificationMulti-Task Learning	CodeCode Available	5
Aspect-Category-Opinion-Sentiment Extraction Using Generative Transformer Model	Jan 18, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	5
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction	Aug 20, 2015	Sentiment AnalysisVisual Sentiment Prediction	CodeCode Available	5
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking	Jun 25, 2017	ClassificationGeneral Classification	CodeCode Available	5
Annotating evaluative sentences for sentiment analysis: a dataset for Norwegian	Sep 1, 2019	Sentiment Analysis	CodeCode Available	5
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container	Aug 15, 2017	Face DetectionFace Recognition	CodeCode Available	5
Distributionally Robust Classifiers in Sentiment Analysis	Oct 20, 2021	Sentiment AnalysisSentiment Classification	CodeCode Available	5
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator	Jun 3, 2018	DecoderParaphrase Generation	CodeCode Available	5
Learning the Difference that Makes a Difference with Counterfactually-Augmented Data	Sep 26, 2019	counterfactualData Augmentation	CodeCode Available	5
Learning from Explanations with Neural Execution Tree	Nov 4, 2019	Data AugmentationMulti-hop Question Answering	CodeCode Available	5
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection	May 30, 2018	Opinion MiningSarcasm Detection	CodeCode Available	5
BERT-Based Sentiment Analysis: A Software Engineering Perspective	Jun 4, 2021	Recommendation SystemsSentiment Analysis	CodeCode Available	5
Aspect-based summarization of pros and cons in unstructured product reviews	Aug 1, 2018	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	CodeCode Available	5
Diverse Few-Shot Text Classification with Multiple Metrics	May 19, 2018	ClassificationFew-Shot Learning	CodeCode Available	5
A Comparative Study of Pre-training and Self-training	Sep 4, 2024	Data AugmentationNatural Language Inference	CodeCode Available	5
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph	Aug 19, 2022	DecoderImage Captioning	CodeCode Available	5
BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives	Jan 10, 2022	Sentiment Analysistext-classification	CodeCode Available	5
BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification	Nov 27, 2023	ClassificationGenre classification	CodeCode Available	5
Distributed Representations of Sentences and Documents	May 16, 2014	Question AnsweringSentiment Analysis	CodeCode Available	5
A Game Theoretic Approach to Class-wise Selective Rationalization	Oct 28, 2019	counterfactualSentiment Analysis	CodeCode Available	5
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict Resolution	May 8, 2025	Sentiment AnalysisSentiment Classification	CodeCode Available	5

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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