Compară metode

	Analiza lizibilității	Analiza sentimentelor	Clasificarea textului	TF-IDF
Domeniu	Mineritul textelor	Mineritul textelor	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1975	—	—	1988
Autorul original	J. Peter Kincaid et al.	—	—	Salton & Buckley
Tip	Text-mining readability scoring task	NLP text-classification task	Supervised NLP classification task	Text vectorization / term-weighting scheme
Sursa seminală	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Denumiri alternative	readability scoring, readability formulas, Flesch-Kincaid analysis, Okunabilirlik Analizi	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Înrudite	3	3	4	3
Rezumat	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	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.