Σύγκριση μεθόδων

	Ανάλυση Συναισθήματος	Τμηματοποίηση Κειμένου
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	—	1997
Δημιουργός	—	Marti A. Hearst (TextTiling)
Τύπος	NLP text-classification task	NLP document-structure / topic-boundary detection
Θεμελιώδης πηγή	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.
Εναλλακτικές ονομασίες	opinion mining, polarity detection, duygu analizi	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)
Συναφείς	3	4
Σύνοψη	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.	Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.