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 2476–2500 of 5630 papers

Title	Date	Tasks	Status
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds	Apr 1, 2017	Named Entity Recognition (NER)Sentiment Analysis	—Unverified
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi	Apr 1, 2021	Sentiment AnalysisSubjectivity Analysis	—Unverified
Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks	Mar 21, 2025	ArticlesBinary Classification	—Unverified
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation	Oct 1, 2018	Sentiment Analysis	—Unverified
CPH: Sentiment analysis of Figurative Language on Twitter \#easypeasy \#not	Jun 1, 2015	Sentiment Analysis	—Unverified
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language	May 1, 2018	Opinion MiningSentiment Analysis	—Unverified
Adjective Intensity and Sentiment Analysis	Sep 1, 2015	Sentiment Analysis	—Unverified
A Comparison of Approaches for Sentiment Classification on Lithuanian Internet Comments	Aug 1, 2013	General ClassificationSentiment Analysis	—Unverified
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology	Jun 11, 2024	Sentiment AnalysisSentiment Classification	—Unverified
Aspect Term Extraction using Graph-based Semi-Supervised Learning	Feb 20, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
COVID-19 sentiment analysis via deep learning during the rise of novel cases	Apr 5, 2021	Language ModelingLanguage Modelling	—Unverified
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method	Apr 1, 2014	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	—Unverified
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets	Oct 14, 2024	Feature ImportanceLanguage Identification	—Unverified
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification	May 21, 2020	BIG-bench Machine LearningClassification	—Unverified
Covid-19 Public Sentiment Analysis for Indian Tweets Classification	Aug 1, 2023	ClassificationSentiment Analysis	—Unverified
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining	May 23, 2020	Chinese Sentiment AnalysisData Visualization	—Unverified
COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations	Dec 22, 2024	Sentiment Analysis	—Unverified
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network	Feb 7, 2019	SentenceSentiment Analysis	—Unverified
A Distant Supervision Approach to Semantic Role Labeling	Jun 1, 2015	Machine TranslationQuestion Answering	—Unverified
COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19	May 12, 2020	Literature MiningSentiment Analysis	—Unverified
Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics	Dec 15, 2015	Sentiment Analysis	—Unverified
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation	Jul 1, 2015	General ClassificationSentiment Analysis	—Unverified
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data	Nov 1, 2019	ClassificationData Augmentation	—Unverified
Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining	Feb 22, 2019	Opinion MiningSentiment Analysis	—Unverified
A multi-task learning network using shared BERT models for aspect-based sentiment analysis	Dec 27, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—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	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