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 1526–1550 of 5630 papers

Title	Date	Tasks	Status
A Survey of Diffusion Models in Natural Language Processing	May 24, 2023	Few-Shot LearningMachine Translation	—Unverified
OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning	May 24, 2023	Data AugmentationFact Checking	CodeCode Available
Prompting Large Language Models for Counterfactual Generation: An Empirical Study	May 24, 2023	counterfactualData Augmentation	—Unverified
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction	May 23, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition	May 23, 2023	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available
Multilingual Large Language Models Are Not (Yet) Code-Switchers	May 23, 2023	BenchmarkingLanguage Identification	—Unverified
Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning	May 23, 2023	Sentiment Analysis	—Unverified
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions	May 23, 2023	Data AugmentationLanguage Modeling	CodeCode Available
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP	May 23, 2023	DiversityMulti-Task Learning	CodeCode Available
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis	May 22, 2023	counterfactualCross-Lingual Transfer	—Unverified
Cross-functional Analysis of Generalisation in Behavioural Learning	May 22, 2023	Paraphrase IdentificationReading Comprehension	CodeCode Available
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals	May 22, 2023	counterfactualData Augmentation	—Unverified
SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News	May 20, 2023	SentenceSentiment Analysis	—Unverified
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages	May 19, 2023	counterfactualSentiment Analysis	—Unverified
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment	May 19, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis	May 18, 2023	Sentiment AnalysisStock Prediction	—Unverified
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification	May 18, 2023	Domain AdaptationMachine Translation	CodeCode Available
Comparing Biases and the Impact of Multilingual Training across Multiple Languages	May 18, 2023	AttributeFairness	—Unverified
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective	May 17, 2023	Event Extractionnamed-entity-recognition	—Unverified
The Weighted Möbius Score: A Unified Framework for Feature Attribution	May 16, 2023	Sentiment Analysis	CodeCode Available
Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study	May 16, 2023	Sentiment Analysis	—Unverified
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning	May 15, 2023	Multimodal Sentiment AnalysisMulti-Task Learning	—Unverified
Multimodal Sentiment Analysis: A Survey	May 12, 2023	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified
Asymmetric feature interaction for interpreting model predictions	May 12, 2023	modelSentiment Analysis	CodeCode Available
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks	May 11, 2023	Explainable Artificial Intelligence (XAI)Sentiment Analysis	CodeCode Available

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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