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 1501–1525 of 5630 papers

Title	Date	Tasks	Status	Hype
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT3	Jul 1, 2022	Data AugmentationFew-Shot Learning	—Unverified	0
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis	Jul 1, 2022	Event ExtractionSentiment Analysis	—Unverified	0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis	Jul 1, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Evaluating Gender Bias Transfer from Film Data	Jul 1, 2022	Dialogue GenerationMachine Translation	—Unverified	0
OPI at SemEval-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment Analysis	Jul 1, 2022	Relation ClassificationSentiment Analysis	CodeCode Available	0
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection	Jul 1, 2022	Sarcasm DetectionSentence	—Unverified	0
AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis	Jul 1, 2022	Sentiment Analysis	—Unverified	0
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model	Jul 1, 2022	PositionQuestion Answering	—Unverified	0
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis	Jul 1, 2022	Data AugmentationLanguage Modeling	—Unverified	0
TechSSN at SemEval-2022 Task 6: Intended Sarcasm Detection using Transformer Models	Jul 1, 2022	Language IdentificationSarcasm Detection	—Unverified	0
SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi	Jul 1, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction	Jul 1, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM	Jul 1, 2022	Sentiment Analysis	—Unverified	0
ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis	Jul 1, 2022	Sentiment Analysis	CodeCode Available	0
ZHIXIAOBAO at SemEval-2022 Task 10: Apporoaching Structured Sentiment with Graph Parsing	Jul 1, 2022	Dependency ParsingSemantic Dependency Parsing	—Unverified	0
TweetNLP: Cutting-Edge Natural Language Processing for Social Media	Jun 29, 2022	Language IdentificationNamed Entity Recognition	CodeCode Available	2
MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations	Jun 28, 2022	Aspect Category Sentiment AnalysisSentiment Analysis	—Unverified	0
Defending Multimodal Fusion Models against Single-Source Adversaries	Jun 25, 2022	Action Recognitionobject-detection	—Unverified	0
Adversarial Self-Attention for Language Understanding	Jun 25, 2022	Machine Reading ComprehensionNamed Entity Recognition (NER)	CodeCode Available	0
Sentiment Analysis with R: Natural Language Processing for Semi-Automated Assessments of Qualitative Data	Jun 25, 2022	Sentiment Analysis	—Unverified	0
The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress	Jun 23, 2022	Emotion RecognitionHumor Detection	CodeCode Available	1
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision	Jun 21, 2022	Emotion ClassificationFew-Shot Learning	—Unverified	0
muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem	Jun 21, 2022	Multilingual text classificationSarcasm Detection	—Unverified	0
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data	Jun 20, 2022	ClassificationDomain Adaptation	CodeCode Available	1
Misspelling Semantics In Thai	Jun 20, 2022	Sentiment Analysis	—Unverified	0

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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