방법 비교

	콘텐츠 분석	담화 분석	텍스트 분류
분야	질적 방법	질적 연구	텍스트 마이닝
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	Systematised through Krippendorff's methodology work; 4th edition 2018	1989 (Fairclough); 1987 (Potter & Wetherell)	—
창시자	Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research	Norman Fairclough; Jonathan Potter and Margaret Wetherell	—
유형	Qualitative / mixed-method research technique	Method	Supervised NLP classification task
원전	Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661	Fairclough, N. (1989). Language and power. Longman.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
별칭	İçerik Analizi, systematic content coding, quantitative content analysis	DA, Critical Discourse Analysis, Discursive Analysis	text categorization, document classification, topic classification, metin sınıflandırma
관련	5	2	4
요약	Content analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.	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.	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.