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 3326–3350 of 5630 papers

Title	Date	Tasks	Status	Hype
Simplifying Graph Convolutional Networks	Feb 19, 2019	Graph RegressionImage Classification	CodeCode Available	1
Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish	Feb 18, 2019	General ClassificationSegmentation	—Unverified	0
Data augmentation for low resource sentiment analysis using generative adversarial networks	Feb 18, 2019	Data AugmentationSentiment Analysis	—Unverified	0
A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine	Feb 17, 2019	Arabic Sentiment Analysisfeature selection	CodeCode Available	0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media	Feb 16, 2019	Data AugmentationNegation	—Unverified	0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square Error	Feb 14, 2019	Sentiment AnalysisTime Series Analysis	CodeCode Available	0
Categorical Metadata Representation for Customized Text Classification	Feb 14, 2019	ClassificationGeneral Classification	CodeCode Available	0
Language Models are Unsupervised Multitask Learners	Feb 14, 2019	Common Sense ReasoningCoreference Resolution	CodeCode Available	1
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities	Feb 13, 2019	Crop Yield PredictionSentiment Analysis	—Unverified	0
Tensor Variable Elimination for Plated Factor Graphs	Feb 8, 2019	Music ModelingProbabilistic Programming	—Unverified	0
Multi-task Learning for Target-dependent Sentiment Classification	Feb 8, 2019	ClassificationGeneral Classification	CodeCode Available	0
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network	Feb 7, 2019	SentenceSentiment Analysis	—Unverified	0
Towards Autoencoding Variational Inference for Aspect-based Opinion Summary	Feb 7, 2019	General ClassificationSentiment Analysis	CodeCode Available	0
Natural Language Processing, Sentiment Analysis and Clinical Analytics	Feb 2, 2019	Sentiment Analysis	—Unverified	0
Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey	Feb 2, 2019	Sentiment AnalysisSurvey	—Unverified	0
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks	Jan 31, 2019	Data AugmentationGeneral Classification	CodeCode Available	2
Multi-Task Deep Neural Networks for Natural Language Understanding	Jan 31, 2019	Domain AdaptationLanguage Modeling	CodeCode Available	0
Tensorized Embedding Layers for Efficient Model Compression	Jan 30, 2019	Language ModellingMachine Translation	CodeCode Available	0
Glyce: Glyph-vectors for Chinese Character Representations	Jan 29, 2019	Chinese Dependency ParsingChinese Named Entity Recognition	CodeCode Available	0
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques	Jan 28, 2019	Sentiment Analysis	—Unverified	0
Language Independent Sequence Labelling for Opinion Target Extraction	Jan 28, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified	0
Squeezed Very Deep Convolutional Neural Networks for Text Classification	Jan 28, 2019	ClassificationGeneral Classification	CodeCode Available	0
Language Model Pre-training for Hierarchical Document Representations	Jan 26, 2019	Document SummarizationExtractive Document Summarization	—Unverified	0
Intrinsically Sparse Long Short-Term Memory Networks	Jan 26, 2019	Model CompressionSentiment Analysis	—Unverified	0
State-Regularized Recurrent Neural Networks	Jan 25, 2019	Language ModelingLanguage Modelling	—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