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 4551–4600 of 5630 papers

Title	Date	Tasks	Status
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification	Jun 1, 2016	Feature EngineeringGeneral Classification	—Unverified
CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings	Jun 1, 2016	Sentiment Analysis	—Unverified
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification	Jun 1, 2016	Active LearningGeneral Classification	—Unverified
OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the ``Real'' World	Jun 1, 2016	Sentiment Analysis	—Unverified
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach	Jun 1, 2016	General ClassificationOpinion Mining	—Unverified
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases	Jun 1, 2016	Sentiment AnalysisStance Detection	—Unverified
SemEval-2016 Task 5: Aspect Based Sentiment Analysis	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
SemEval-2016 Task 14: Semantic Taxonomy Enrichment	Jun 1, 2016	Information RetrievalSemantic Textual Similarity	—Unverified
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best--Worst Scaling	Jun 1, 2016	Sentiment AnalysisStance Detection	—Unverified
Hierarchical Attention Networks for Document Classification	Jun 1, 2016	Citation Intent ClassificationClassification	CodeCode Available
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision	Jun 1, 2016	General ClassificationSentence Embedding	—Unverified
Hit Songs' Sentiments Harness Public Mood \& Predict Stock Market	Jun 1, 2016	Sentiment Analysis	—Unverified
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification	Jun 1, 2016	General ClassificationSentiment Analysis	—Unverified
BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Aspect Category Detection	—Unverified
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Building Chinese Affective Resources in Valence-Arousal Dimensions	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	—Unverified
English Resource Semantics	Jun 1, 2016	Machine TranslationNatural Language Inference	—Unverified
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification	Jun 1, 2016	Document ClassificationLanguage Modeling	CodeCode Available
How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis	Jun 1, 2016	Entity LinkingSemantic Role Labeling	—Unverified
Distinguishing Literal and Non-Literal Usage of German Particle Verbs	Jun 1, 2016	General ClassificationLanguage Identification	—Unverified
JU\_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines	Jun 1, 2016	Information RetrievalNatural Language Inference	—Unverified
NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis	Jun 1, 2016	Sentiment AnalysisTwitter Sentiment Analysis	—Unverified
Linguistic Understanding of Complaints and Praises in User Reviews	Jun 1, 2016	Sentiment Analysis	—Unverified
YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network	Jun 1, 2016	General ClassificationSentiment Analysis	—Unverified
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter	Jun 1, 2016	Feature EngineeringLanguage Modeling	—Unverified
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library	Jun 1, 2016	Document ClassificationOpinion Mining	—Unverified
Self-Reflective Sentiment Analysis	Jun 1, 2016	Sentiment AnalysisWord Embeddings	—Unverified
An Hymn of an even Deeper Sentiment Analysis	Jun 1, 2016	Common Sense ReasoningSemantic Role Labeling	—Unverified
NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets	Jun 1, 2016	Sentiment Analysis	—Unverified
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms	Jun 1, 2016	Sentiment Analysis	—Unverified
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Sentiment Analysis	—Unverified
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter	Jun 1, 2016	Emotion RecognitionGeneral Classification	—Unverified
``Why Should I Trust You?'': Explaining the Predictions of Any Classifier	Jun 1, 2016	Document ClassificationFeature Engineering	CodeCode Available
Scalable Statistical Relational Learning for NLP	Jun 1, 2016	Coreference ResolutionRelational Reasoning	—Unverified
TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
Automatic Triage of Mental Health Forum Posts	Jun 1, 2016	Sentiment AnalysisText Classification	—Unverified
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale	Jun 1, 2016	Sentiment AnalysisWord Embeddings	—Unverified
IDI@NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation	Jun 1, 2016	Sentiment AnalysisStance Detection	—Unverified
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Feature Engineering	—Unverified
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation	Jun 1, 2016	Sentiment Analysis	—Unverified
Modelling Valence and Arousal in Facebook posts	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Emotion Classification	—Unverified
The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection	Jun 1, 2016	Document SummarizationRelation	—Unverified
Automatically Inferring Implicit Properties in Similes	Jun 1, 2016	Sentiment AnalysisWord Embeddings	—Unverified
IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence	Jun 1, 2016	Aspect-Based Sentiment Analysis (ABSA)Sentence	—Unverified
Domain Adaptation of Polarity Lexicon combining Term Frequency and Bootstrapping	Jun 1, 2016	Domain AdaptationSentiment Analysis	—Unverified
Right-truncatable Neural Word Embeddings	Jun 1, 2016	Dependency ParsingMachine Translation	—Unverified
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis	Jun 1, 2016	Sentiment AnalysisTwitter Sentiment Analysis	—Unverified
ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews	Jun 1, 2016	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—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