Text Regression — Predicting Numbers from Text
Text-Based Regression · Also known as: text-as-data regression, predicting numeric outcomes from text, Metin Tabanlı Regresyon
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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When to use it
Use text regression when you have a text predictor and a continuous numeric outcome you want to predict from it, with at least roughly fifty documents to learn from. The text must first be vectorised, and dimensionality reduction is often helpful given how wide text feature spaces are. It does not assume normally distributed inputs. With far fewer documents, or with no numeric target to regress on, the method is not appropriate.
Strengths & limitations
- Lets a continuous outcome be predicted directly from text, with no manual coding of document content.
- Works with several feature types — TF-IDF, embeddings, or n-grams — so the representation can match the corpus.
- Does not require normally distributed inputs, fitting the sparse, high-dimensional features text produces.
- Needs a reasonable sample (around fifty documents or more) to estimate stable coefficients.
- Text must be vectorised first, and the very high dimensionality usually calls for dimensionality reduction.
- Predictive quality depends on how well the chosen text features carry signal about the numeric target.
Frequently asked
What kind of target does text regression predict?
A continuous numeric variable — for example a price, a rating, or a sentiment score. If the target is a category rather than a number, text classification is the matching method instead.
Which text features should I use as predictors?
TF-IDF scores, word or document embeddings, and n-gram counts are all standard choices. The best representation depends on the corpus and the signal you expect the wording to carry about the target.
How much data do I need?
The method expects on the order of fifty documents or more so the regression can estimate stable coefficients. Because text features are high-dimensional, dimensionality reduction is often advisable even when the sample is adequate.
Why is dimensionality reduction recommended?
Text vectorisation produces very wide, sparse feature matrices. Reducing the dimensionality compresses redundant features, stabilises the fit, and helps the model generalise instead of memorising rare wordings.
Sources
- Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574. DOI: 10.1257/jel.20181020 ↗
- 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 ↗
How to cite this page
ScholarGate. (2026, June 1). Text-Based Regression. ScholarGate. https://scholargate.app/en/text-mining/text-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- BERT EmbeddingsText mining↔ compare
- Sentiment AnalysisText mining↔ compare
- Text ClassificationText mining↔ compare
- TF-IDFText mining↔ compare