Process / pipeline
Sentiment Analysis — Opinion Mining
Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.
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Sources
- Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI: 10.1561/1500000011 ↗
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Referenced by
Argument MiningAspect-Based Sentiment AnalysisAutomated Essay ScoringAutomatic Text EvaluationBERT EmbeddingsBERTopicClinical Text MiningCo-occurrence AnalysisContent AnalysisCoreference ResolutionCross-lingual Text AnalysisDialogue Act ClassificationDiscourse ParsingDoc2VecDomain AdaptationEmotion DetectionEvent DetectionFake News DetectionFew-Shot Text ClassificationGender Bias DetectionHallucination DetectionHate Speech DetectionImplicit Sentiment AnalysisIntent DetectionKeyword ExtractionLanguage IdentificationLexical DiversityLexical SubstitutionLexicon-Based Sentiment AnalysisLinguistic Acceptability AssessmentMachine Reading ComprehensionMachine TranslationMorphological AnalysisMulti-Document SummarizationMultimodal NLPNegation DetectionOpinion MiningParaphrase DetectionPropaganda DetectionQuestion AnsweringReadability AnalysisScientific Text MiningSemantic ParsingSemantic SimilaritySocial Media NLPSpeculation DetectionSpelling and Grammar CheckStance DetectionSubjectivity DetectionText ClassificationText Coherence ScoringText Complexity AnalysisText DeduplicationText Frequency AnalysisText InfillingText Network AnalysisText NormalizationText RegressionText SegmentationText SummarizationTextual EntailmentTF-IDFTopic Modeling (LDA)Word Sense DisambiguationZero-Shot Classification