방법 비교

	Automated Content Analysis	Sentiment Analysis in Communication
분야	Communication	Communication
계열	Process / pipeline	Process / pipeline
기원 연도	2013	2010
창시자	Justin Grimmer & Brandon Stewart (synthesis)	Adapted into communication research from NLP / opinion mining
유형	Computational pipeline for measuring features of large text corpora	Automated classification of message valence/tone
원전	Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.	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.
별칭	Computational content analysis, Text-as-data analysis, Automated text analysis, Otomatik İçerik Analizi	Opinion mining in communication, Tone analysis, Media sentiment analysis, İletişimde Duygu Analizi
관련	4	5
요약	Automated content analysis is the computational measurement of text features at a scale impossible by hand, using natural-language processing and machine learning to classify, scale, or discover the content of large corpora. Synthesized for the social sciences by Grimmer and Stewart's 2013 'Text as Data,' it spans supervised classification, unsupervised discovery, and scaling, all unified by the principle that automated methods augment but do not replace careful human judgment and validation.	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.