So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phân tích diễn ngôn× | Lý thuyết Nền tảng× | Phân tích Cảm xúc× | |
|---|---|---|---|
| Lĩnh vực≠ | Nghiên cứu định tính | Nghiên cứu định tính | Khai phá văn bản |
| Họ | Process / pipeline | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1989 (Fairclough); 1987 (Potter & Wetherell) | 1967 | — |
| Người khởi xướng≠ | Norman Fairclough; Jonathan Potter and Margaret Wetherell | Barney Glaser and Anselm Strauss | — |
| Loại≠ | Method | Method | NLP text-classification task |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác≠ | DA, Critical Discourse Analysis, Discursive Analysis | GT, Grounded Theory Approach | opinion mining, polarity detection, duygu analizi |
| Liên quan≠ | 2 | 3 | 3 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|
|