Jämför metoder

	Innehållsanalys	Grounded Theory	Sentimentanalys
Ämnesområde	Kvalitativa metoder	Kvalitativ forskning	Textutvinning
Familj	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	Systematised through Krippendorff's methodology work; 4th edition 2018	1967	—
Upphovsperson	Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research	Barney Glaser and Anselm Strauss	—
Typ	Qualitative / mixed-method research technique	Method	NLP text-classification task
Ursprungskälla	Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. ISBN: 978-1506395661	Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Alias	İçerik Analizi, systematic content coding, quantitative content analysis	GT, Grounded Theory Approach	opinion mining, polarity detection, duygu analizi
Närliggande	5	3	3
Sammanfattning	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.	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.	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.