Linganisha mbinu

	Uchambuzi wa Maudhui	Grounded Theory	Uainishaji wa Maandishi
Nyanja	Mbinu za Kimaelezo	Utafiti wa Kimaelezo	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	Systematised through Krippendorff's methodology work; 4th edition 2018	1967	—
Mwanzilishi	Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research	Barney Glaser and Anselm Strauss	—
Aina	Qualitative / mixed-method research technique	Method	Supervised NLP classification task
Chanzo asilia	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Majina mbadala	İçerik Analizi, systematic content coding, quantitative content analysis	GT, Grounded Theory Approach	text categorization, document classification, topic classification, metin sınıflandırma
Zinazohusiana	5	3	4
Muhtasari	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.	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.