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| Speculation Detection× | 感情分析× | |
|---|---|---|
| 分野 | テキストマイニング | テキストマイニング |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1996 (lexicon approach); 2010 (CoNLL shared task) | — |
| 提唱者≠ | Hyland, K. (lexicon-based framing, 1996); Farkas et al. (shared-task benchmark, 2010) | — |
| 種類 | NLP text-classification task | NLP text-classification task |
| 原典≠ | Hyland, K. (1996). Writing Without Conviction? Hedging in Science Research Articles. Applied Linguistics, 17(4), 433-454. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| 別名≠ | hedging detection, epistemic modality analysis, hedge detection, Belirsizlik / Spekülasyon Tespiti (Hedging) | opinion mining, polarity detection, duygu analizi |
| 関連≠ | 5 | 3 |
| 概要≠ | Speculation detection, also known as hedging analysis, is a natural-language-processing task that identifies epistemic uncertainty markers — words and phrases such as 'may', 'possibly', 'it is suggested that' — within scientific, biomedical, and news texts. Formalised by Hyland (1996) for scientific writing and benchmarked by the CoNLL-2010 shared task, the method reveals where authors signal incomplete knowledge, tentativeness, or distance from a claim rather than asserting facts directly. | 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. |
| ScholarGateデータセット ↗ |
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