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 151–175 of 5630 papers

Title	Date	Tasks	Status	Hype
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series	Jan 23, 2023	Emotion ClassificationMultivariate Time Series Forecasting	CodeCode Available	1
Tsetlin Machine Embedding: Representing Words Using Logical Expressions	Jan 2, 2023	Document ClassificationMachine Translation	CodeCode Available	1
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction	Nov 28, 2022	Aspect Sentiment Triplet ExtractionSentence	CodeCode Available	1
TEMPERA: Test-Time Prompting via Reinforcement Learning	Nov 21, 2022	Few-Shot LearningNatural Language Inference	CodeCode Available	1
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure	Nov 14, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts	Nov 12, 2022	Language ModellingMultimodal Sentiment Analysis	CodeCode Available	1
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis	Nov 10, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Fixing Model Bugs with Natural Language Patches	Nov 7, 2022	modelRelation Extraction	CodeCode Available	1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages	Nov 7, 2022	Active LearningLanguage Modeling	CodeCode Available	1
Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations	Oct 31, 2022	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	1
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations	Oct 29, 2022	ClusteringSentence	CodeCode Available	1
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation	Oct 19, 2022	Aspect-Based Sentiment Analysis (ABSA)Data Augmentation	CodeCode Available	1
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis	Oct 18, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	1
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks	Oct 14, 2022	backdoor defenseSentiment Analysis	CodeCode Available	1
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis	Oct 12, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis	Oct 11, 2022	Multimodal Sentiment AnalysisSentiment Analysis	CodeCode Available	1
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection	Oct 9, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework	Oct 6, 2022	ClassificationData Augmentation	CodeCode Available	1
Explaining Patterns in Data with Language Models via Interpretable Autoprompting	Oct 4, 2022	Explanation GenerationNatural Language Understanding	CodeCode Available	1
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification	Oct 1, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline	Oct 1, 2022	Multimodal Sentiment AnalysisSentiment Analysis	CodeCode Available	1
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets	Sep 28, 2022	Epidemiologynamed-entity-recognition	CodeCode Available	1
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs	Sep 27, 2022	ArticlesContrastive Learning	CodeCode Available	1
Augmenting Interpretable Models with LLMs during Training	Sep 23, 2022	Additive modelsLanguage Modelling	CodeCode Available	1
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions	Sep 18, 2022	object-detectionObject Detection	CodeCode Available	1

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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	T5-11B	Accuracy	97.5	—	Unverified
3	MT-DNN-SMART	Accuracy	97.5	—	Unverified
4	T5-3B	Accuracy	97.4	—	Unverified
5	MUPPET Roberta Large	Accuracy	97.4	—	Unverified
6	StructBERTRoBERTa ensemble	Accuracy	97.1	—	Unverified
7	ALBERT	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