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 2576–2600 of 5630 papers

Title	Date	Tasks	Status	Hype
Metaphor Detection using Deep Contextualized Word Embeddings	Sep 26, 2020	Sentiment AnalysisWord Embeddings	—Unverified	0
Empirical Study of Text Augmentation on Social Media Text in Vietnamese	Sep 25, 2020	Data AugmentationGeneral Classification	CodeCode Available	0
Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques	Sep 24, 2020	Recommendation SystemsSentiment Analysis	—Unverified	0
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis	Sep 22, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Subjective Metrics-based Cloud Market Performance Prediction	Sep 21, 2020	BIG-bench Machine LearningPrediction	—Unverified	0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers	Sep 21, 2020	Language ModelingLanguage Modelling	—Unverified	0
An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management	Sep 20, 2020	BIG-bench Machine LearningDialogue Management	—Unverified	0
Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation	Sep 19, 2020	Machine TranslationSentiment Analysis	CodeCode Available	1
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations	Sep 19, 2020	Adversarial AttackNatural Language Inference	—Unverified	0
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis	Sep 18, 2020	Extract AspectMulti-Task Learning	CodeCode Available	0
Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis	Sep 16, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Arabic Opinion Mining Using a Hybrid Recommender System Approach	Sep 16, 2020	Opinion MiningRecommendation Systems	—Unverified	0
Analysis of Models for Decentralized and Collaborative AI on Blockchain	Sep 14, 2020	Sentiment Analysis	CodeCode Available	1
Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector	Sep 12, 2020	Sentiment AnalysisSentiment Classification	—Unverified	0
Country Image in COVID-19 Pandemic: A Case Study of China	Sep 12, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Improving Indonesian Text Classification Using Multilingual Language Model	Sep 12, 2020	ClassificationGeneral Classification	CodeCode Available	1
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing	Sep 10, 2020	ClassificationGeneral Classification	CodeCode Available	1
Pay Attention when Required	Sep 9, 2020	Language ModellingParaphrase Identification	CodeCode Available	0
Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews	Sep 9, 2020	ClassificationGeneral Classification	—Unverified	0
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function	Sep 8, 2020	ClassificationGeneral Classification	CodeCode Available	1
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification	Sep 8, 2020	ClassificationGeneral Classification	—Unverified	0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce	Sep 7, 2020	Aspect ExtractionDenoising	—Unverified	0
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis	Sep 7, 2020	Machine TranslationMultimodal Sentiment Analysis	—Unverified	0
NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier	Sep 7, 2020	Sentiment Analysis	—Unverified	0
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning	Sep 6, 2020	Multi-Task LearningOpinion Mining	—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	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