Proksch, Sven-Oliver ORCID: 0000-0002-6130-6498, Wratil, Christopher ORCID: 0000-0002-7339-9628 and Waeckerle, Jens (2019). Testing the Validity of Automatic Speech Recognition for Political Text Analysis. Polit. Anal., 27 (3). S. 339 - 360. CAMBRIDGE: CAMBRIDGE UNIV PRESS. ISSN 1476-4989

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Abstract

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 . 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.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Proksch, Sven-OliverUNSPECIFIEDorcid.org/0000-0002-6130-6498UNSPECIFIED
Wratil, ChristopherUNSPECIFIEDorcid.org/0000-0002-7339-9628UNSPECIFIED
Waeckerle, JensUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-136303
DOI: 10.1017/pan.2018.62
Journal or Publication Title: Polit. Anal.
Volume: 27
Number: 3
Page Range: S. 339 - 360
Date: 2019
Publisher: CAMBRIDGE UNIV PRESS
Place of Publication: CAMBRIDGE
ISSN: 1476-4989
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
BARGAINING POWER; ELECTION; POSITIONS; SENTIMENT; WORDS; MODEL; NEWS; VOTEMultiple languages
Political ScienceMultiple languages
Refereed: Yes
URI: http://kups.ub.uni-koeln.de/id/eprint/13630

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