Usporedite metode

	Modeliranje tema	Grupisanje dokumenata	Analiza sentimenta	TF-IDF
Područje	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	2003	—	—	1988
Tvorac	Blei, Ng & Jordan	—	—	Salton & Buckley
Vrsta	Generative probabilistic topic model	Unsupervised text-mining task	NLP text-classification task	Text vectorization / term-weighting scheme
Temeljni izvor	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Drugi nazivi	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Srodne	4	4	3	3
Sažetak	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.	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.