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Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

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  • Gavin Abercrombie

    (University of Manchester)

  • Riza Batista-Navarro

    (University of Manchester)

Abstract

Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions, and policy preferences of elected politicians. They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. As a result, the problem of computational sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. The existing research is scattered across publications from various fields and venues. In this article, we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed, and the positions taken by speakers in parliamentary (and other legislative) debates. In this review, we discuss the existing research with regard to the aims and objectives of the researchers who work in this area, the automatic analysis tasks which they undertake, and the approaches and methods which they use. We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

Suggested Citation

  • Gavin Abercrombie & Riza Batista-Navarro, 2020. "Sentiment and position-taking analysis of parliamentary debates: a systematic literature review," Journal of Computational Social Science, Springer, vol. 3(1), pages 245-270, April.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:1:d:10.1007_s42001-019-00060-w
    DOI: 10.1007/s42001-019-00060-w
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    2. Alex Luscombe & Kevin Dick & Kevin Walby, 2022. "Algorithmic thinking in the public interest: navigating technical, legal, and ethical hurdles to web scraping in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1023-1044, June.
    3. Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
    4. Mikko Moilanen & Stein Østbye, 2021. "Doublespeak? Sustainability in the Arctic—A Text Mining Analysis of Norwegian Parliamentary Speeches," Sustainability, MDPI, vol. 13(16), pages 1-15, August.
    5. Müller-Hansen, Finn & Lee, Yuan Ting & Callaghan, Max & Jankin, Slava & Minx, Jan C., 2022. "The German coal debate on Twitter: Reactions to a corporate policy process," Energy Policy, Elsevier, vol. 169(C).
    6. Albina Latifi & Viktoriia Naboka-Krell & Peter Tillmann & Peter Winker, 2023. "Fiscal Policy in the Bundestag: Textual Analysis and Macroeconomic Effects," MAGKS Papers on Economics 202307, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

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