विधियों की तुलना करें

	रूप-विश्लेषण	भाव विश्लेषण	पाठ विभाजन
क्षेत्र	पाठ खनन	पाठ खनन	पाठ खनन
परिवार	Process / pipeline	Process / pipeline	Process / pipeline
उद्भव वर्ष	1980	—	1997
प्रवर्तक	M.F. Porter (Porter stemmer)	—	Marti A. Hearst (TextTiling)
प्रकार	Text-normalisation preprocessing task	NLP text-classification task	NLP document-structure / topic-boundary detection
मौलिक स्रोत	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	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.
उपनाम	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	opinion mining, polarity detection, duygu analizi	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)
संबंधित	4	3	4
सारांश	Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.	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.