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 4176–4200 of 5630 papers

Title	Date	Tasks	Status
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm	Apr 1, 2017	General ClassificationSentiment Analysis	—Unverified
Online Limited Memory Neural-Linear Bandits	Jan 1, 2021	Efficient ExplorationMulti-Armed Bandits	—Unverified
Online Optimization Methods for the Quantification Problem	May 13, 2016	EpidemiologySentiment Analysis	—Unverified
Only text? only image? or both? Predicting sentiment of internet memes	Dec 1, 2020	ClassificationMeme Classification	—Unverified
On Multilingual Encoder Language Model Compression for Low-Resource Languages	May 22, 2025	Knowledge DistillationLanguage Modeling	—Unverified
On predictability of rare events leveraging social media: a machine learning perspective	Feb 20, 2015	BIG-bench Machine LearningSentiment Analysis	—Unverified
On Prompt Sensitivity of ChatGPT in Affective Computing	Mar 20, 2024	Prompt EngineeringSarcasm Detection	—Unverified
On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?	Dec 21, 2023	Sentiment Analysis	—Unverified
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter	May 1, 2014	ClassificationDocument Classification	—Unverified
On the Automatic Learning of Sentiment Lexicons	May 1, 2015	Sentiment AnalysisSentiment Classification	—Unverified
On the ``Calligraphy'' of Books	Aug 1, 2017	Authorship AttributionMachine Translation	—Unverified
On the Challenges of Sentiment Analysis for Dynamic Events	Oct 6, 2017	Emotion RecognitionMarketing	—Unverified
On the Cost of Model-Serving Frameworks: An Experimental Evaluation	Nov 15, 2024	image-classificationImage Classification	—Unverified
On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis	Oct 16, 2021	ArticlesAspect-Based Sentiment Analysis	—Unverified
On the Distribution of Lexical Features at Multiple Levels of Analysis	Jul 1, 2017	Document ClassificationSentiment Analysis	—Unverified
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers	Jun 2, 2021	QuantizationQuestion Answering	—Unverified
On the effectiveness of feature set augmentation using clusters of word embeddings	May 3, 2017	ClassificationFeature Engineering	—Unverified
On the Effect of Word Order on Cross-lingual Sentiment Analysis	Jun 13, 2019	Cross-Lingual Sentiment ClassificationGeneral Classification	—Unverified
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations	Oct 13, 2022	Information RetrievalRetrieval	—Unverified
On the Impact of Sentiment and Emotion Based Features in Detecting Online Sexual Predators	Jul 1, 2012	Sentiment Analysis	—Unverified
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets	Nov 1, 2020	BenchmarkingSentiment Analysis	—Unverified
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training	Apr 19, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
On the Robustness of Self-Attentive Models	Jul 1, 2019	Machine TranslationSentiment Analysis	—Unverified
On the Robustness of Sentiment Analysis for Stock Price Forecasting	Jan 1, 2021	BIG-bench Machine LearningObject Recognition	—Unverified
On the Vector Representation of Utterances in Dialogue Context	May 1, 2018	Document ClassificationIntent Detection	—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