Сравнение методов

	Анализ частоты текста	Анализ тональности	TF-IDF
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1949	—	1988
Автор метода	George K. Zipf (frequency-distribution foundation)	—	Salton & Buckley
Тип	Descriptive text-mining analysis	NLP text-classification task	Text vectorization / term-weighting scheme
Основополагающий источник	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.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Другие названия	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Связанные	4	3	3
Сводка	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.	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.