Σύγκριση μεθόδων

	Ανάλυση Συχνότητας Κειμένου	Ανάλυση Συναισθήματος
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1949	—
Δημιουργός	George K. Zipf (frequency-distribution foundation)	—
Τύπος	Descriptive text-mining analysis	NLP text-classification task
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	opinion mining, polarity detection, duygu analizi
Συναφείς	4	3
Σύνοψη	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.