Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It

By Matthew J. Denny, Arthur Spirling. Political Analysis, 2017

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, General Social Sciences, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/95e0c468a19afc6173053234c7fe660033363ffb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bryan T. Kelly
2. Matt Taddy
3. Matthew Gentzkow
4. B. Seegmiller
5. Alan E. Gelfand
6. Dario Caldara
7. Lawrence D. W. Schmidt
8. Matteo Iacoviello

In General Social Sciences

1. Matt Taddy
2. B. Seegmiller
3. Dario Caldara
4. Lawrence D. W. Schmidt
5. Matteo Iacoviello

In Political Science and International Relations

1. Bryan T. Kelly
2. Matt Taddy
3. Matthew Gentzkow
4. Stefano DellaVigna
5. Jesse M. Shapiro
6. Ro’ee Levy
7. Yi Lu
8. David Strömberg

Citations in the corpus

• Text as Data (2017, background): “2Denny and Spirling (2018) discuss the sensitivity of unsupervised text analysis methods such as topic modeling to preprocessing steps.”