Process / pipeline

Text Regression — Predicting Numbers from Text

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.

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Sources

  1. Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574. DOI: 10.1257/jel.20181020
  2. Taddy, M. (2013). Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression. Technometrics, 55(4), 415-425. DOI: 10.1080/00401706.2013.778791

Related methods

Referenced by

ScholarGateText Regression (Text-Based Regression). Retrieved 2026-06-04 from https://scholargate.app/tr/text-mining/text-regression