Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Дискурс-аналіз× | Теорія, ґрунтована на даних (Grounded Theory)× | Сентимент-аналіз× | |
|---|---|---|---|
| Галузь≠ | Якісні дослідження | Якісні дослідження | Інтелектуальний аналіз тексту |
| Родина | Process / pipeline | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1989 (Fairclough); 1987 (Potter & Wetherell) | 1967 | — |
| Автор методу≠ | Norman Fairclough; Jonathan Potter and Margaret Wetherell | Barney Glaser and Anselm Strauss | — |
| Тип≠ | Method | Method | NLP text-classification task |
| Основоположне джерело≠ | Fairclough, N. (1989). Language and power. Longman. link ↗ | Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Інші назви≠ | DA, Critical Discourse Analysis, Discursive Analysis | GT, Grounded Theory Approach | opinion mining, polarity detection, duygu analizi |
| Пов'язані≠ | 2 | 3 | 3 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
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