Vertaile menetelmiä

	Tekstiregressio – numeeristen arvojen ennustaminen tekstistä	TF-IDF
Tieteenala	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	—	1988
Kehittäjä	—	Salton & Buckley
Tyyppi	Supervised regression on text features	Text vectorization / term-weighting scheme
Alkuperäislähde	Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Rinnakkaisnimet	text-as-data regression, predicting numeric outcomes from text, Metin Tabanlı Regresyon	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Liittyvät	4	3
Tiivistelmä	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.