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 2051–2075 of 5630 papers

Title	Date	Tasks	Status
Exploring the Effects of Word Roots for Arabic Sentiment Analysis	Oct 1, 2013	Arabic Sentiment AnalysisSentiment Analysis	—Unverified
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities	Jun 1, 2018	Interpretable Machine LearningSentence Classification	—Unverified
A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT	Jun 6, 2022	Decision MakingSentiment Analysis	—Unverified
A Data-driven Neural Network Architecture for Sentiment Analysis	Jun 30, 2020	Sentiment Analysis	—Unverified
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter	Aug 1, 2014	Feature EngineeringSentiment Analysis	—Unverified
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases	Dec 1, 2017	ClusteringFeature Engineering	—Unverified
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques	May 20, 2024	Ordinal ClassificationSentiment Analysis	—Unverified
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning	Aug 1, 2014	ClassificationFeature Engineering	—Unverified
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations	Aug 21, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation	Aug 30, 2021	Sentiment Analysis	—Unverified
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis	Aug 1, 2018	ClassificationGeneral Classification	—Unverified
A Sentiment Analysis Approach to the Prediction of Market Volatility	Dec 10, 2020	ManagementPortfolio Optimization	—Unverified
A Combined Pattern-based and Distributional Approach for Automatic Hypernym Detection in Dutch.	Sep 1, 2013	Information RetrievalNatural Language Inference	—Unverified
Exploring Multimodal Sentiment Analysis via CBAM Attention and Double-layer BiLSTM Architecture	Mar 26, 2023	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified
Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis	Oct 26, 2022	Sentiment Analysis	—Unverified
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set	Apr 1, 2021	ClusteringSentiment Analysis	—Unverified
A Sentiment-aligned Topic Model for Product Aspect Rating Prediction	Oct 1, 2014	Opinion MiningSentiment Analysis	—Unverified
Exploring Fine-Grained Emotion Detection in Tweets	Jun 1, 2016	Emotion ClassificationGeneral Classification	—Unverified
A Semi-supervised Multi-task Learning Approach to Classify Customer Contact Intents	Jun 10, 2021	Classificationintent-classification	—Unverified
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency	Oct 1, 2014	ClusteringSentiment Analysis	—Unverified
A Leveled Reading Corpus of Modern Standard Arabic	May 1, 2018	Document ClassificationMachine Translation	—Unverified
Exploring Implicit Sentiment Evoked by Fine-grained News Events	Apr 1, 2021	ArticlesCollaborative Filtering	—Unverified
A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention	Jul 27, 2024	Fake News DetectionSelf-Learning	—Unverified
Cluster-based Prediction of User Ratings for Stylistic Surface Realisation	Apr 1, 2014	Recommendation SystemsSentiment Analysis	—Unverified
A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations	Aug 1, 2013	Sentiment Analysis	—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