방법 비교

	콘텐츠 분석	담화 분석	감성 분석
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	NLP text-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.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
별칭	İçerik Analizi, systematic content coding, quantitative content analysis	DA, Critical Discourse Analysis, Discursive Analysis	opinion mining, polarity detection, duygu analizi
관련	5	2	3
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.	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.