Porovnat metody

	Analýza čitelnosti	Analýza sentimentu	TF-IDF
Obor	Dolování textu	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1975	—	1988
Tvůrce	J. Peter Kincaid et al.	—	Salton & Buckley
Typ	Text-mining readability scoring task	NLP text-classification task	Text vectorization / term-weighting scheme
Původní zdroj	Kincaid, J.P., Fishburne, R.P., Rogers, R.L. & Chissom, B.S. (1975). Derivation of New Readability Formulas for Navy Enlisted Personnel. Naval Technical Training Command.	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.
Další názvy	readability scoring, readability formulas, Flesch-Kincaid analysis, Okunabilirlik Analizi	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Příbuzné	3	3	3
Shrnutí	Readability analysis measures how well a text suits its intended audience by applying established readability formulas such as Flesch-Kincaid and Gunning Fog. The modern formula family was derived by Kincaid and colleagues in 1975, and it turns prose into a single score or target reading-grade level that signals how easy the text is to read.	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.