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 journalArticle

    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

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    Keywords

    • descriptive statistics
    • statistical analysis of texts
    • unsupervised learning

    ASJC Scopus subject areas

    • Sociology and Political Science
    • Political Science and International Relations

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