방법 비교

	텍스트 회귀	BERT 임베딩
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	—	2019
창시자	—	Devlin, Chang, Lee & Toutanova (Google AI)
유형	Supervised regression on text features	Contextual transformer text-representation method
원전	Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
별칭	text-as-data regression, predicting numeric outcomes from text, Metin Tabanlı Regresyon	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri
관련	4	4
요약	Text-based regression predicts a continuous target variable using features extracted from text — TF-IDF scores, embeddings, or n-grams — as the independent variables. Building on the text-as-data programme consolidated by Gentzkow, Kelly and Taddy (2019), it lets a numeric outcome such as a price, a rating, or a sentiment score be estimated directly from documents, and is widely used in social-science, economics, and finance applications.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.