Сравнение методов

	Обнаружение отрицания	Анализ тональности
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	2001 (NegEx); scope learning formalised by 2009	—
Автор метода	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	—
Тип	NLP information-extraction task	NLP text-classification task
Основополагающий источник	Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., & Buchanan, B.G. (2001). A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of the American Medical Informatics Association, 8(6), 606-614.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Другие названия	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	opinion mining, polarity detection, duygu analizi
Связанные	6	3
Сводка	Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and extended to scope learning in biomedical literature by Morante and Daelemans (2009), the method is essential wherever the difference between a finding being present and its being explicitly ruled out carries real consequences.	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.