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 3426–3450 of 5630 papers

Title	Date	Tasks	Status
Improving Minor Opinion Polarity Classification with Named Entity Analysis (L'apport des Entit\'es Nomm\'ees pour la classification des opinions minoritaires) [in French]	Jun 1, 2013	ClassificationGeneral Classification	—Unverified
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network	Oct 1, 2018	ClassificationEmotion Classification	—Unverified
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge	Nov 1, 2019	Emotion ClassificationGeneral Classification	—Unverified
Improving Multimodal Accuracy Through Modality Pre-training and Attention	Nov 11, 2020	Emotion RecognitionSentiment Analysis	—Unverified
Improving Multimodal fusion via Mutual Dependency Maximisation	Aug 31, 2021	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation	Dec 4, 2023	Contrastive LearningMultimodal Sentiment Analysis	—Unverified
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding	Apr 20, 2019	Ensemble LearningKnowledge Distillation	—Unverified
Improving Opinion-Target Extraction with Character-Level Word Embeddings	Sep 19, 2017	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Improving Sentiment Analysis in Arabic Using Word Representation	Feb 28, 2018	General ClassificationSentiment Analysis	—Unverified
Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data	Sep 1, 2013	Machine TranslationSentiment Analysis	—Unverified
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation	Oct 7, 2020	Data AugmentationSentiment Analysis	—Unverified
Improving Sentiment Analysis with Biofeedback Data	May 1, 2020	Emotion RecognitionSentence	—Unverified
Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings	Sep 29, 2021	ClassificationSentiment Analysis	—Unverified
Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes	Jul 1, 2015	Opinion MiningSentiment Analysis	—Unverified
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation	Sep 10, 2019	Data AugmentationGeneral Classification	—Unverified
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis	Oct 28, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough	Feb 18, 2023	AttributeNatural Language Inference	—Unverified
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set	Aug 25, 2018	Dialect IdentificationGeneral Classification	—Unverified
Improving Twitter Community Detection through Contextual Sentiment Analysis	Aug 1, 2016	Community DetectionSentiment Analysis	—Unverified
Improving Twitter Named Entity Recognition using Word Representations	Jul 1, 2015	named-entity-recognitionNamed Entity Recognition	—Unverified
Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training	Jun 1, 2014	Sentiment AnalysisStock Prediction	—Unverified
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings	Nov 1, 2016	General ClassificationSentiment Analysis	—Unverified
Improving Unimodal Inference with Multimodal Transformers	Nov 16, 2023	Emotion RecognitionGesture Recognition	—Unverified
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models	Nov 14, 2021	Sentiment Analysis	—Unverified
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning	Sep 1, 2017	Emotion RecognitionSentence	—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