Comparar métodos

	Regresión con texto	TF-IDF
Campo	Minería de texto	Minería de texto
Familia	Process / pipeline	Process / pipeline
Año de origen	—	1988
Autor original	—	Salton & Buckley
Tipo	Supervised regression on text features	Text vectorization / term-weighting scheme
Fuente seminal	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.
Alias	text-as-data regression, predicting numeric outcomes from text, Metin Tabanlı Regresyon	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionados	4	3
Resumen	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.