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| Phát hiện ngôn từ kích động thù địch× | Phân tích Cảm xúc× | |
|---|---|---|
| Lĩnh vực | Khai phá văn bản | Khai phá văn bản |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời | — | — |
| Người khởi xướng | — | — |
| Loại | NLP text-classification task | NLP text-classification task |
| Công trình gốc≠ | Davidson, T., Warmsley, D., Macy, M. & Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM, 11(1), 512-515. DOI ↗ | 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 | offensive language detection, toxic content detection, Nefret Söylemi Tespiti | opinion mining, polarity detection, duygu analizi |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | Hate speech detection is a natural-language-processing task that automatically identifies hateful, offensive, or harmful text on social media and online platforms. The task was sharpened by Davidson and colleagues (2017), who showed why separating genuine hate speech from merely offensive language is a hard, distinct classification problem rather than a single toxicity score. | 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 ↗ |
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