방법 비교

	Dictionary-Based Text Analysis	Automated Content Analysis
분야	Communication	Communication
계열	Process / pipeline	Process / pipeline
기원 연도	2003	2013
창시자	Lexicon tradition (Pennebaker LIWC; General Inquirer)	Justin Grimmer & Brandon Stewart (synthesis)
유형	Word-count text measurement against predefined category dictionaries	Computational pipeline for measuring features of large text corpora
원전	Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577.	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.
별칭	Lexicon-based text analysis, Word-count text analysis, Dictionary method for content analysis, Sözlük Tabanlı Metin Analizi	Computational content analysis, Text-as-data analysis, Automated text analysis, Otomatik İçerik Analizi
관련	4	4
요약	Dictionary-based text analysis measures concepts in text by counting how often words belonging to predefined category lists — dictionaries — appear in each document. It is the workhorse lexicon method behind tools like LIWC and the General Inquirer, prized for its transparency and scalability: a category score is simply the share of a document's words that match the category's word list.	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.