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 1351–1375 of 5630 papers

Title	Date	Tasks	Status	Hype
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models	Oct 24, 2022	Cross-Lingual TransferHate Speech Detection	CodeCode Available	0
Entity-level Sentiment Analysis in Contact Center Telephone Conversations	Oct 24, 2022	Sentiment Analysis	—Unverified	0
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation	Oct 22, 2022	counterfactualData Augmentation	CodeCode Available	0
Volatility forecasting using Deep Learning and sentiment analysis	Oct 22, 2022	Deep LearningSentiment Analysis	—Unverified	0
A Benchmark Study of Contrastive Learning for Arabic Social Meaning	Oct 22, 2022	Contrastive LearningDialect Identification	CodeCode Available	0
TCAB: A Large-Scale Text Classification Attack Benchmark	Oct 21, 2022	Abuse DetectionClassification	CodeCode Available	0
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals	Oct 21, 2022	Classificationcounterfactual	CodeCode Available	0
Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques	Oct 21, 2022	Sentiment Analysis	—Unverified	0
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation	Oct 19, 2022	Aspect-Based Sentiment Analysis (ABSA)Data Augmentation	CodeCode Available	1
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language	Oct 19, 2022	ArticlesClassification	—Unverified	0
Large Discourse Treebanks from Scalable Distant Supervision	Oct 18, 2022	Discourse ParsingSentiment Analysis	—Unverified	0
NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task	Oct 18, 2022	Dialect IdentificationSentiment Analysis	CodeCode Available	0
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis	Oct 18, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	1
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models	Oct 18, 2022	Language ModellingSentence	CodeCode Available	8
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs	Oct 18, 2022	Aspect ExtractionKnowledge Graphs	CodeCode Available	4
Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study	Oct 17, 2022	MisinformationSentiment Analysis	—Unverified	0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning	Oct 17, 2022	Federated Learningimage-classification	CodeCode Available	0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective	Oct 16, 2022	Coreference ResolutionMultiple-choice	CodeCode Available	4
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks	Oct 14, 2022	backdoor defenseSentiment Analysis	CodeCode Available	1
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons	Oct 13, 2022	ARCSentiment Analysis	—Unverified	0
Best Practices in the Creation and Use of Emotion Lexicons	Oct 13, 2022	EthicsSentiment Analysis	—Unverified	0
Rethinking Annotation: Can Language Learners Contribute?	Oct 13, 2022	Machine Reading Comprehensionnamed-entity-recognition	—Unverified	0
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods	Oct 13, 2022	Abstractive Text SummarizationFeature Importance	CodeCode Available	0
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations	Oct 13, 2022	Information RetrievalRetrieval	—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