Σύγκριση μεθόδων

	Ανάλυση Δικτύων Κειμένου	Ανάλυση Συναισθήματος
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2011 (Paranyushkin); 2005 (Diesner & Carley)	—
Δημιουργός	Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley	—
Τύπος	Text-mining network method	NLP text-classification task
Θεμελιώδης πηγή	Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Εναλλακτικές ονομασίες	semantic network analysis, word co-occurrence network, Metin Ağ Analizi (Text Network Analysis)	opinion mining, polarity detection, duygu analizi
Συναφείς	4	3
Σύνοψη	Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text.	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.