Confronta i metodi

	Topic Modeling	Analisi del Sentimento
Campo	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2003	—
Ideatore	Blei, Ng & Jordan	—
Tipo	Generative probabilistic topic model	NLP text-classification task
Fonte seminale	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Alias	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	opinion mining, polarity detection, duygu analizi
Correlati	4	3
Sintesi	Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.	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.