So sánh phương pháp

	Phân tích đồng xuất hiện	Phân tích Cảm xúc	Topic Modeling (Mô hình hóa chủ đề)
Lĩnh vực	Khai phá văn bản	Khai phá văn bản	Học sâu
Họ	Process / pipeline	Process / pipeline	Machine learning
Năm ra đời	1957	—	1999–2003
Người khởi xướng	J.R. Firth (distributional principle)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Loại	Text-mining / distributional-semantics technique	NLP text-classification task	Unsupervised generative probabilistic model
Công trình gốc	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Tên gọi khác	word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Liên quan	4	3	5
Tóm tắt	Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.