Linganisha mbinu

	Uchanganuzi wa Marudio ya Maandishi	Uchanganuzi wa Hisia	Uundaji wa Mada
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Ujifunzaji wa Kina
Familia	Process / pipeline	Process / pipeline	Machine learning
Mwaka wa asili	1949	—	1999–2003
Mwanzilishi	George K. Zipf (frequency-distribution foundation)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Aina	Descriptive text-mining analysis	NLP text-classification task	Unsupervised generative probabilistic model
Chanzo asilia	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	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.
Majina mbadala	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Zinazohusiana	4	3	5
Muhtasari	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.	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.