Porównaj metody

	Analiza sentymentu	Wykrywanie subiektywności	Analiza złożoności tekstu
Dziedzina	Eksploracja tekstu	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	—	—	—
Twórca	—	—	—
Typ	NLP text-classification task	NLP text-classification task	Linguistic-feature measurement pipeline
Źródło pierwotne	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.	Vajjala, S. & Meurers, D. (2014). Readability Assessment for Text Simplification: From Analysing Documents to Identifying Sentential Simplifications. International Journal of Applied Linguistics, 165(2), 194-222.
Inne nazwy	opinion mining, polarity detection, duygu analizi	subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection)	readability analysis, linguistic complexity assessment, Metin Karmaşıklığı Analizi
Pokrewne	3	3	2
Podsumowanie	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.	Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis.	Text complexity analysis measures the linguistic difficulty of a text along dimensions such as syntactic complexity (sentence length, embedded clauses), lexical density, and referential chains. Grounded in readability research consolidated by Vajjala and Meurers (2014) and Crossley and colleagues (2011), it turns prose into quantitative scores that estimate how hard a document is to read.