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 2301–2325 of 5630 papers

Title	Date	Tasks	Status	Hype
An Operator Theoretic Approach for Analyzing Sequence Neural Networks	Feb 15, 2021	ECG ClassificationSentiment Analysis	CodeCode Available	0
TI-Capsule: Capsule Network for Stock Exchange Prediction	Feb 15, 2021	MarketingPrediction	—Unverified	0
Emoji-Based Transfer Learning for Sentiment Tasks	Feb 12, 2021	Hate Speech DetectionSentiment Analysis	CodeCode Available	0
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis	Feb 9, 2021	Multimodal Sentiment AnalysisMulti-Task Learning	CodeCode Available	1
An open access NLP dataset for Arabic dialects : Data collection, labeling, and model construction	Feb 7, 2021	Sentiment Analysis	CodeCode Available	1
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention	Feb 7, 2021	Natural Language InferenceQuestion Answering	CodeCode Available	1
HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition	Feb 3, 2021	Active LearningEmotion Recognition	—Unverified	0
A Stochastic Time Series Model for Predicting Financial Trends using NLP	Feb 2, 2021	Generative Adversarial NetworkSentiment Analysis	—Unverified	0
Scaling Federated Learning for Fine-tuning of Large Language Models	Feb 1, 2021	Federated LearningSentiment Analysis	—Unverified	0
[Re] Neural Networks Fail to Learn Periodic Functions and How to Fix It	Jan 31, 2021	Sentiment Analysis	CodeCode Available	1
If you've got it, flaunt it: Making the most of fine-grained sentiment annotations	Jan 30, 2021	General ClassificationSentiment Analysis	—Unverified	0
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models	Jan 30, 2021	ClassificationGeneral Classification	—Unverified	0
BERTaú: Itaú BERT for digital customer service	Jan 28, 2021	ChatbotInformation Retrieval	—Unverified	0
Muppet: Massive Multi-task Representations with Pre-Finetuning	Jan 26, 2021	Abstractive Text SummarizationCommon Sense Reasoning	CodeCode Available	0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks	Jan 26, 2021	Cross-Lingual TransferLanguage Modeling	—Unverified	0
Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets	Jan 25, 2021	Domain AdaptationSentiment Analysis	—Unverified	0
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh	Jan 24, 2021	ArticlesSentiment Analysis	—Unverified	0
Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com	Jan 23, 2021	Sentiment Analysis	—Unverified	0
Arabic aspect based sentiment analysis using bidirectional GRU based models	Jan 23, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild	Jan 23, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text	Jan 22, 2021	SentenceSentiment Analysis	CodeCode Available	0
The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach	Jan 20, 2021	BIG-bench Machine LearningFeature Engineering	CodeCode Available	1
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data	Jan 16, 2021	BIG-bench Machine LearningSentiment Analysis	—Unverified	0
The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements	Jan 15, 2021	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified	0
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis	Jan 12, 2021	BenchmarkingDecision Making	—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	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