مقایسهٔ روش‌ها

	تجزیه گفتمان	تحلیل احساسات	طبقه‌بندی متن
حوزه	متن‌کاوی	متن‌کاوی	متن‌کاوی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	1988 (RST); 2008 (PDTB 2.0)	—	—
پدیدآور	Mann & Thompson (RST); Prasad et al. (PDTB)	—	—
نوع	NLP discourse-structure analysis task	NLP text-classification task	Supervised NLP classification task
منبع بنیادین	Mann, W. C. & Thompson, S. A. (1988). Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3), 243-281.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
نام‌های دیگر	rhetorical structure analysis, RST parsing, PDTB parsing, Söylem Ayrıştırma (Discourse Parsing)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
مرتبط	3	3	4
خلاصه	Discourse parsing is a natural-language-processing task that models the rhetorical relations between sentences and paragraphs of a text — relations such as cause, contrast, and elaboration — and represents them as a tree structure. It works within established frameworks, principally Rhetorical Structure Theory (RST), introduced by Mann and Thompson in 1988, and the Penn Discourse TreeBank (PDTB), released by Prasad and colleagues in 2008.	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 classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.