IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v3y2020i1d10.1007_s42001-019-00060-w.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-019-00060-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-019-00060-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    2. Diermeier, Daniel & Godbout, Jean-François & Yu, Bei & Kaufmann, Stefan, 2012. "Language and Ideology in Congress," British Journal of Political Science, Cambridge University Press, vol. 42(1), pages 31-55, January.
    3. Ludovic Rheault & Kaspar Beelen & Christopher Cochrane & Graeme Hirst, 2016. "Measuring Emotion in Parliamentary Debates with Automated Textual Analysis," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-18, December.
    4. Kim, In Song & Londregan, John & Ratkovic, Marc, 2018. "Estimating Spatial Preferences from Votes and Text," Political Analysis, Cambridge University Press, vol. 26(2), pages 210-229, April.
    5. Lowe, Will & Benoit, Kenneth, 2013. "Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark," Political Analysis, Cambridge University Press, vol. 21(3), pages 298-313, July.
    6. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    7. Proksch, Sven-Oliver & Slapin, Jonathan B., 2010. "Position Taking in European Parliament Speeches," British Journal of Political Science, Cambridge University Press, vol. 40(3), pages 587-611, July.
    8. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    9. Jacob Jensen & Ethan Kaplan & Suresh Naidu & Laurence Wilse-Samson, 2012. "Political Polarization and the Dynamics of Political Language: Evidence from 130 Years of Partisan Speech," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 1-81.
    10. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    11. Monroe, Burt L. & Colaresi, Michael P. & Quinn, Kevin M., 2008. "Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict," Political Analysis, Cambridge University Press, vol. 16(4), pages 372-403.
    12. Matt Taddy, 2013. "Multinomial Inverse Regression for Text Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 755-770, September.
    13. Jacob Jensen & Ethan Kaplan & Suresh Naidu & Laurence Wilse-Samson, 2012. "Political Polarization and the Dynamics of Political Language: Evidence from 130 Years of Partisan Speech," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 45(2 (Fall)), pages 1-81.
    14. Iliyan R. Iliev & Xin Huang & Yulia R. Gel, 2019. "Political rhetoric through the lens of non‐parametric statistics: are our legislators that different?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 583-604, February.
    15. Schwarz, Daniel & Traber, Denise & Benoit, Kenneth, 2017. "Estimating Intra-Party Preferences: Comparing Speeches to Votes," Political Science Research and Methods, Cambridge University Press, vol. 5(2), pages 379-396, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Latifi, Albina & Naboka-Krell, Viktoriia & Tillmann, Peter & Winker, Peter, 2024. "Fiscal policy in the Bundestag: Textual analysis and macroeconomic effects," European Economic Review, Elsevier, vol. 168(C).
    2. Mithani, Murad A., 2024. "Nationalistic political rhetoric: measurement and preliminary insights," Journal of International Management, Elsevier, vol. 30(2).
    3. 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.
    4. 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).
    5. 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.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caroline Le Pennec, 2020. "Strategic Campaign Communication: Evidence from 30,000 Candidate Manifestos," SoDa Laboratories Working Paper Series 2020-05, Monash University, SoDa Laboratories.
    2. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    3. Renáta Németh, 2023. "A scoping review on the use of natural language processing in research on political polarization: trends and research prospects," Journal of Computational Social Science, Springer, vol. 6(1), pages 289-313, April.
    4. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    5. Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2022. "Sharks and minnows in a shoal of words: Measuring latent ideological positions based on text mining techniques," European Journal of Political Economy, Elsevier, vol. 75(C).
    6. Born, Andreas & Janssen, Aljoscha, 2022. "Does a district mandate matter for the behavior of politicians? An analysis of roll-call votes and parliamentary speeches," European Journal of Political Economy, Elsevier, vol. 71(C).
    7. Elliott Ash & Germain Gauthier & Philine Widmer, 2021. "RELATIO: Text Semantics Capture Political and Economic Narratives," Papers 2108.01720, arXiv.org, revised Apr 2022.
    8. Gloria Gennaro & Elliott Ash, 2022. "Emotion and Reason in Political Language," The Economic Journal, Royal Economic Society, vol. 132(643), pages 1037-1059.
    9. Weiss, Max & Zoorob, Michael, 2021. "Political frames of public health crises: Discussing the opioid epidemic in the US Congress," Social Science & Medicine, Elsevier, vol. 281(C).
    10. Sabina J Sloman & Daniel M Oppenheimer & Simon DeDeo, 2021. "Can we detect conditioned variation in political speech? two kinds of discussion and types of conversation," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    11. H. Andrew Schwartz & Lyle H. Ungar, 2015. "Data-Driven Content Analysis of Social Media," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 78-94, May.
    12. Adriana Bunea & Raimondas Ibenskas, 2015. "Quantitative text analysis and the study of EU lobbying and interest groups," European Union Politics, , vol. 16(3), pages 429-455, September.
    13. Snorre Sylvester Frid-Nielsen, 2018. "Human rights or security? Positions on asylum in European Parliament speeches," European Union Politics, , vol. 19(2), pages 344-362, June.
    14. Heike Klüver, 2015. "The promises of quantitative text analysis in interest group research: A reply to Bunea and Ibenskas," European Union Politics, , vol. 16(3), pages 456-466, September.
    15. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    16. Alexander Herzog & Slava Mikhaylov, 2010. "A new Database of Parliamentary Debates in Ireland, 1922--2008," The Institute for International Integration Studies Discussion Paper Series iiisdp338, IIIS, revised Jul 2010.
    17. Greene, Zac & Ceron, Andrea & Schumacher, Gijs & Fazekas, Zoltan, 2016. "The Nuts and Bolts of Automated Text Analysis. Comparing Different Document Pre-Processing Techniques in Four Countries," OSF Preprints ghxj8, Center for Open Science.
    18. Born, Andreas & Janssen, Aljoscha, 2020. "Does a District-Vote Matter for the Behavior of Politicians? A Textual Analysis of Parliamentary Speeches," Working Paper Series 1320, Research Institute of Industrial Economics.
    19. Anna Calissano & Simone Vantini & Marika Arena, 2020. "Monitoring rare categories in sentiment and opinion analysis: a Milan mega event on Twitter platform," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 787-812, December.
    20. Weifeng Zhong, 2016. "The candidates in their own words: A textual analysis of 2016 president primary debates," AEI Economic Perspectives, American Enterprise Institute, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:3:y:2020:i:1:d:10.1007_s42001-019-00060-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.