Comparar métodos

	Análisis de Frecuencia de Texto	Análisis de Sentimiento	Modelado de Temas
Campo	Minería de texto	Minería de texto	Aprendizaje profundo
Familia	Process / pipeline	Process / pipeline	Machine learning
Año de origen	1949	—	1999–2003
Autor original	George K. Zipf (frequency-distribution foundation)	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipo	Descriptive text-mining analysis	NLP text-classification task	Unsupervised generative probabilistic model
Fuente seminal	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.
Alias	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
Relacionados	4	3	5
Resumen	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.