Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modélisation thématique× | Analyse des sentiments× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2003 | — |
| Auteur d'origine≠ | Blei, Ng & Jordan | — |
| Type≠ | Generative probabilistic topic model | NLP text-classification task |
| Source fondatrice≠ | Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias | LDA, latent Dirichlet allocation, Konu Modelleme — LDA | opinion mining, polarity detection, duygu analizi |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | 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. |
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