Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, and What to Do about It

Matthew J. Denny, Arthur Spirling

Research output: Contribution to journalArticlepeer-review

239 Scopus citations

Abstract

Despite the popularity of unsupervised techniques for political science text-as-data research, the importance and implications of preprocessing decisions in this domain have received scant systematic attention. Yet, as we show, such decisions have profound effects on the results of real models for real data. We argue that substantive theory is typically too vague to be of use for feature selection, and that the supervised literature is not necessarily a helpful source of advice. To aid researchers working in unsupervised settings, we introduce a statistical procedure and software that examines the sensitivity of findings under alternate preprocessing regimes. This approach complements a researcher's substantive understanding of a problem by providing a characterization of the variability changes in preprocessing choices may induce when analyzing a particular dataset. In making scholars aware of the degree to which their results are likely to be sensitive to their preprocessing decisions, it AIDS replication efforts.

Original languageEnglish (US)
Pages (from-to)168-189
Number of pages22
JournalPolitical Analysis
Volume26
Issue number2
DOIs
StatePublished - Apr 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

Keywords

  • descriptive statistics
  • statistical analysis of texts
  • unsupervised learning

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