مقایسهٔ روش‌ها

	تحلیل فراوانی متن	تنوع واژگانی	مدل‌سازی موضوعی
حوزه	متن‌کاوی	متن‌کاوی	یادگیری عمیق
خانواده	Process / pipeline	Process / pipeline	Machine learning
سال پیدایش	1949	—	1999–2003
پدیدآور	George K. Zipf (frequency-distribution foundation)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
نوع	Descriptive text-mining analysis	Text quantification / lexical richness measurement	Unsupervised generative probabilistic model
منبع بنیادین	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
نام‌های دیگر	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
مرتبط	4	3	5
خلاصه	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.	Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.	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.