Σύγκριση μεθόδων

	Sentiment Analysis in Communication	Ανάλυση Συναισθήματος
Πεδίο	Communication	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2010	—
Δημιουργός	Adapted into communication research from NLP / opinion mining	—
Τύπος	Automated classification of message valence/tone	NLP text-classification task
Θεμελιώδης πηγή	Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Εναλλακτικές ονομασίες	Opinion mining in communication, Tone analysis, Media sentiment analysis, İletişimde Duygu Analizi	opinion mining, polarity detection, duygu analizi
Συναφείς	5	3
Σύνοψη	Sentiment analysis is the automated estimation of the valence — positive, negative, or neutral tone — of communication messages, adapted from natural-language processing into a core measurement technique for media and communication research. It lets scholars quantify the tone of news coverage, the affect of social-media discourse, or audience reactions across corpora far too large for hand coding, while treating tone as a measurable, validatable construct.	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.