방법 비교

	Supervised Text Classification	감성 분석
분야	Political Science	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	2013	—
창시자	Machine-learning classification tradition (formalized for political text by Grimmer & Stewart; category-proportion variant by Hopkins & King)	—
유형	Supervised machine-learning classification of documents	NLP text-classification task
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.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
별칭	Supervised document classification, Text categorization, Automated text coding, Supervised content analysis	opinion mining, polarity detection, duygu analizi
관련	5	3
요약	Supervised text classification trains a statistical model on documents that humans have hand-labeled, then uses it to assign categories — topic, tone, position, relevance — to the much larger set of unlabeled documents. Unlike dictionary methods, which apply a fixed word list, a supervised classifier learns from examples which textual features predict each category, so it can capture context-dependent and non-obvious cues. Grimmer and Stewart present it as a core text-as-data workflow, and a key insight is that for many political-science questions the goal is not perfect document-by-document labels but accurate estimates of category proportions across a corpus.	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.