方法对比

	文本情感检测	对话行为分类	情感分析	文本分类
领域	文本挖掘	文本挖掘	文本挖掘	文本挖掘
方法族	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1992	1997–2000	—	—
提出者	Paul Ekman (basic-emotions theory)	Stolcke et al.; Jurafsky et al.	—	—
类型	NLP text-classification task	NLP utterance-classification task	NLP text-classification task	Supervised NLP classification task
开创性文献	Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.	Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
别名	emotion recognition, emotion classification, Duygu/His Tespiti (Emotion Detection)	dialogue act tagging, speech act classification, Diyalog Eylem Sınıflandırma (Dialogue Act Classification)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
相关	3	4	3	4
摘要	Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a small set of universal basic emotions, going beyond a simple positive/negative split to attach a specific emotion label to each piece of text.	Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.