Linganisha mbinu

	Uchanganuzi wa Hisia	TF-IDF	Uundaji wa Mada
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Ujifunzaji wa Kina
Familia	Process / pipeline	Process / pipeline	Machine learning
Mwaka wa asili	—	1988	1999–2003
Mwanzilishi	—	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Aina	NLP text-classification task	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Chanzo asilia	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Majina mbadala	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Zinazohusiana	3	3	5
Muhtasari	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.	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.