Testing the Validity of Automatic Speech Recognition for Political Text Analysis

The analysis of political texts from parliamentary speeches, party manifestos, social media, or press releases forms the basis of major and growing fields in political science, not least since advances in “text-as-data” methods have rendered the analysis of large text corpora straightforward. Howeve...

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Bibliographic Details
Published in:Political analysis 2019-07, Vol.27 (3), p.339-359
Main Authors: Proksch, Sven-Oliver, Wratil, Christopher, Wäckerle, Jens
Format: Article
Language:English
Subjects:
Congresses and conventions
Data analysis
Demonstrations & protests
Information sources
Intellectuals
International relations
Legislatures
Mass media
News media
Political manifesto
Political parties
Political science
Researchers
Simulation
Social media
Social networks
Speech
Speeches
Talk shows
Text analysis
Validity
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Summary:The analysis of political texts from parliamentary speeches, party manifestos, social media, or press releases forms the basis of major and growing fields in political science, not least since advances in “text-as-data” methods have rendered the analysis of large text corpora straightforward. However, a lot of sources of political speech are not regularly transcribed, and their on-demand transcription by humans is prohibitively expensive for research purposes. This class includes political speech in certain legislatures, during political party conferences as well as television interviews and talk shows. We showcase how scholars can use automatic speech recognition systems to analyze such speech with quantitative text analysis models of the “bag-of-words” variety. To probe results for robustness to transcription error, we present an original “word error rate simulation” (WERSIM) procedure implemented in $R$ . We demonstrate the potential of automatic speech recognition to address open questions in political science with two substantive applications and discuss its limitations and practical challenges.
ISSN:1047-1987
1476-4989
DOI:10.1017/pan.2018.62