Σύγκριση μεθόδων

	Κανονικοποίηση Κειμένου	Ανάλυση Συναισθήματος
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	—	—
Δημιουργός	—	—
Τύπος	NLP preprocessing pipeline	NLP text-classification task
Θεμελιώδης πηγή	Baldwin, T. & Li, Y. (2015). An In-depth Analysis of the Effect of Text Normalization in Twitter. NAACL-HLT 2015.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Εναλλακτικές ονομασίες	Metin Normalleştirme, noisy-text normalization, text standardisation, lexical normalisation	opinion mining, polarity detection, duygu analizi
Συναφείς	3	3
Σύνοψη	Text normalization is an NLP preprocessing pipeline that converts noisy, abbreviated, or misspelled text — such as SMS messages, social-media posts, and OCR output — into a clean, standardised form. It is a prerequisite step for virtually every downstream NLP task, ensuring that inconsistent surface forms do not degrade tokenisation, parsing, or classification. The method gained systematic academic treatment through Baldwin and Li (2015) and Sproat and Jaitly (2017).	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.