विधियों की तुलना करें

	विमर्श विश्लेषण (Discourse Analysis)	ग्राउंडेड थ्योरी	पाठ वर्गीकरण
क्षेत्र	गुणात्मक अनुसंधान	गुणात्मक अनुसंधान	पाठ खनन
परिवार	Process / pipeline	Process / pipeline	Process / pipeline
उद्भव वर्ष	1989 (Fairclough); 1987 (Potter & Wetherell)	1967	—
प्रवर्तक	Norman Fairclough; Jonathan Potter and Margaret Wetherell	Barney Glaser and Anselm Strauss	—
प्रकार	Method	Method	Supervised NLP classification task
मौलिक स्रोत	Fairclough, N. (1989). Language and power. Longman.	Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
उपनाम	DA, Critical Discourse Analysis, Discursive Analysis	GT, Grounded Theory Approach	text categorization, document classification, topic classification, metin sınıflandırma
संबंधित	2	3	4
सारांश	Discourse analysis is a qualitative research methodology that examines how language, communication, and power shape meaning, identity, and social reality. Developed across linguistics, sociology, and psychology (particularly by Norman Fairclough and Jonathan Potter), discourse analysis goes beyond content to analyze language use as a social practice that constitutes and reflects power relations, ideologies, and social structures.	Grounded Theory (GT) is a systematic qualitative research methodology in which theory emerges directly from data through iterative analysis, rather than being imposed before data collection. Developed by Barney Glaser and Anselm Strauss in 1967, GT prioritizes generating explanatory frameworks grounded in evidence.	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.