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 4201–4250 of 5630 papers

Title	Date	Tasks	Status
On the Distribution of Lexical Features at Multiple Levels of Analysis	Jul 1, 2017	Document ClassificationSentiment Analysis	—Unverified
Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task	Jul 1, 2017	NegationOutlier Detection	—Unverified
English Event Detection With Translated Language Features	Jul 1, 2017	Event DetectionEvent Extraction	—Unverified
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network	Jul 1, 2017	EEGElectroencephalogram (EEG)	—Unverified
Linguistically Regularized LSTM for Sentiment Classification	Jul 1, 2017	ClassificationGeneral Classification	—Unverified
Parser Adaptation for Social Media by Integrating Normalization	Jul 1, 2017	Domain AdaptationNamed Entity Recognition (NER)	—Unverified
Active Sentiment Domain Adaptation	Jul 1, 2017	Active LearningDomain Adaptation	—Unverified
Benben: A Chinese Intelligent Conversational Robot	Jul 1, 2017	Chinese Word SegmentationDependency Parsing	—Unverified
Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast	Jul 1, 2017	Sentiment Analysis	—Unverified
Deep Pyramid Convolutional Neural Networks for Text Categorization	Jul 1, 2017	Sentiment AnalysisSentiment Classification	CodeCode Available
Improved Word Representation Learning with Sememes	Jul 1, 2017	Common Sense ReasoningLanguage Modeling	CodeCode Available
An Unsupervised Neural Attention Model for Aspect Extraction	Jul 1, 2017	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling	Jul 1, 2017	Document ClassificationInformation Retrieval	—Unverified
A Two-Stage Parsing Method for Text-Level Discourse Analysis	Jul 1, 2017	Dependency ParsingDocument Summarization	CodeCode Available
Context-Dependent Sentiment Analysis in User-Generated Videos	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Improving Distributed Representations of Tweets - Present and Future	Jul 1, 2017	Information RetrievalRepresentation Learning	—Unverified
Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction	Jul 1, 2017	PredictionSentiment Analysis	—Unverified
Improving Distributed Representations of Tweets - Present and Future	Jun 29, 2017	Representation LearningSentiment Analysis	—Unverified
A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking	Jun 25, 2017	ClassificationGeneral Classification	CodeCode Available
Explaining Recurrent Neural Network Predictions in Sentiment Analysis	Jun 22, 2017	General ClassificationInterpretable Machine Learning	—Unverified
Stance Detection in Turkish Tweets	Jun 21, 2017	Sentiment AnalysisStance Detection	—Unverified
How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis	Jun 7, 2017	Sentiment Analysis	—Unverified
Deep Learning for Hate Speech Detection in Tweets	Jun 1, 2017	16kDeep Learning	CodeCode Available
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations	May 31, 2017	ClassificationClustering	—Unverified
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis	May 29, 2017	ClusteringEnsemble Learning	—Unverified
Contextual Explanation Networks	May 29, 2017	Image ClassificationInterpretability Techniques for Deep Learning	CodeCode Available
Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge	May 29, 2017	Sentiment Analysis	—Unverified
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks	May 26, 2017	Domain AdaptationSentiment Analysis	CodeCode Available
TwiInsight: Discovering Topics and Sentiments from Social Media Datasets	May 23, 2017	Sentiment Analysis	—Unverified
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis	May 22, 2017	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available
Universal Dependencies Parsing for Colloquial Singaporean English	May 18, 2017	Dependency ParsingSentiment Analysis	CodeCode Available
On Identifying Disaster-Related Tweets: Matching-based or Learning-based?	May 4, 2017	Sentiment Analysis	CodeCode Available
Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter	May 3, 2017	General Classificationregression	—Unverified
On the effectiveness of feature set augmentation using clusters of word embeddings	May 3, 2017	ClassificationFeature Engineering	—Unverified
Optimizing a PoS Tagset for Norwegian Dependency Parsing	May 1, 2017	Dependency ParsingFeature Engineering	—Unverified
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning	Apr 27, 2017	Deep LearningReading Comprehension	—Unverified
Learning to Skim Text	Apr 23, 2017	Document ClassificationGeneral Classification	CodeCode Available
BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs	Apr 20, 2017	Sentiment AnalysisTwitter Sentiment Analysis	CodeCode Available
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees	Apr 18, 2017	Data AugmentationMarketing	—Unverified
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings	Apr 17, 2017	Sentiment AnalysisWord Embeddings	CodeCode Available
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning	Apr 12, 2017	Sentiment AnalysisTransfer Learning	—Unverified
What we really want to find by Sentiment Analysis: The Relationship between Computational Models and Psychological State	Apr 11, 2017	Sentiment AnalysisTransfer Learning	—Unverified
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis	Apr 7, 2017	General ClassificationSentiment Analysis	CodeCode Available
An Automated Text Categorization Framework based on Hyperparameter Optimization	Apr 6, 2017	Authorship AttributionGeneral Classification	CodeCode Available
Learning to Generate Reviews and Discovering Sentiment	Apr 5, 2017	Sentiment AnalysisSubjectivity Analysis	CodeCode Available
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention	Apr 4, 2017	ClassificationGeneral Classification	—Unverified
Interpretation of Semantic Tweet Representations	Apr 4, 2017	Feature EngineeringProperty Prediction	CodeCode Available
Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study	Apr 1, 2017	Sentiment Analysis	—Unverified
Multilingual Training of Crosslingual Word Embeddings	Apr 1, 2017	Bilingual Lexicon InductionDependency Parsing	—Unverified
Lingmotif: Sentiment Analysis for the Digital Humanities	Apr 1, 2017	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