So sánh phương pháp

	Phân loại hành vi hội thoại	Phát hiện ý định	Phân tích Cảm xúc
Lĩnh vực	Khai phá văn bản	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	1997–2000	—	—
Người khởi xướng	Stolcke et al.; Jurafsky et al.	—	—
Loại	NLP utterance-classification task	NLP / NLU text-classification task	NLP text-classification task
Công trình gốc	Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.	Larson, S. et al. (2019). An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. EMNLP.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Tên gọi khác	dialogue act tagging, speech act classification, Diyalog Eylem Sınıflandırma (Dialogue Act Classification)	intent classification, intent recognition, Niyet Tespiti (Intent Detection)	opinion mining, polarity detection, duygu analizi
Liên quan	4	4	3
Tóm tắt	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.	Intent detection is a natural-language-understanding task that classifies the purpose behind a user utterance — such as making a reservation, asking for information, or filing a complaint — into one of a set of predefined intent classes. It is a core NLU component of conversational interfaces and customer-service automation systems, drawing on the benchmarks of Larson et al. (2019) and Casanueva et al. (2020).	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.