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Sentiment analysis of political communication: combining a dictionary approach with crowdcoding

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  • Martin Haselmayer

    (University of Vienna)

  • Marcelo Jenny

    (University of Vienna)

Abstract

Sentiment is important in studies of news values, public opinion, negative campaigning or political polarization and an explosive expansion of digital textual data and fast progress in automated text analysis provide vast opportunities for innovative social science research. Unfortunately, tools currently available for automated sentiment analysis are mostly restricted to English texts and require considerable contextual adaption to produce valid results. We present a procedure for collecting fine-grained sentiment scores through crowdcoding to build a negative sentiment dictionary in a language and for a domain of choice. The dictionary enables the analysis of large text corpora that resource-intensive hand-coding struggles to cope with. We calculate the tonality of sentences from dictionary words and we validate these estimates with results from manual coding. The results show that the crowdbased dictionary provides efficient and valid measurement of sentiment. Empirical examples illustrate its use by analyzing the tonality of party statements and media reports.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:6:d:10.1007_s11135-016-0412-4
    DOI: 10.1007/s11135-016-0412-4
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    4. Patrick Hirsch & Lars P. Feld & Ekkehard A. Köhler & Tobias Thomas, 2024. "“Whatever It Takes!” How Tonality of TV-News Affected Government Bond Yield Spreads during the European Debt Crisis," CESifo Working Paper Series 10980, CESifo.
    5. Katarina Böttcher & Kerstin Lopatta, 2020. "Gender-Sensitive Language in German Annual Reports," Journal of Management and Sustainability, Canadian Center of Science and Education, vol. 8(4), pages 1-1, March.
    6. Wolfinger, Julia & Köhler, Ekkehard A. & Feld, Lars P. & Thomas, Tobias, 2018. "57 Channels (And Nothin On): Does TV-News on the Eurozone affect Government Bond Yield Spreads?," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181610, Verein für Socialpolitik / German Economic Association.
    7. Hirsch, Patrick & Köhler, Ekkehard A. & Feld, Lars P. & Thomas, Tobias, 2020. ""Whatever it takes!": How tonality of TV-news affects government bond yield spreads during crises," Freiburg Discussion Papers on Constitutional Economics 20/9, Walter Eucken Institut e.V..
    8. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. Hiroki Takikawa & Takuto Sakamoto, 2020. "The moral–emotional foundations of political discourse: a comparative analysis of the speech records of the U.S. and the Japanese legislatures," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 547-566, April.
    10. Shesen Guo & Ganzhou Zhang, 2020. "Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries," SAGE Open, , vol. 10(3), pages 21582440209, August.
    11. Katja Pietrzyck & Nora Berke & Vanessa Wendel & Julia Steinhoff-Wagner & Sebastian Jarzębowski & Brigitte Petersen, 2021. "Understanding the Importance of International Quality Standards Regarding Global Trade in Food and Agricultural Products: Analysis of the German Media," Agriculture, MDPI, vol. 11(4), pages 1-20, April.
    12. Hugo Oriola & Matthieu Picault, 2023. "Opportunistic Political Central Bank Coverage: Does media coverage of ECB's Monetary Policy Impacts German Political Parties' Popularity?," EconomiX Working Papers 2023-30, University of Paris Nanterre, EconomiX.
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    14. Rauh, Christian, 2018. "Validating a sentiment dictionary for German political language—a workbench note," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(4), pages 319-343.
    15. Zobel, Malisa & Lehmann, Pola, 2018. "Positions and saliency of immigration in party manifestos: A novel dataset using crowd coding," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 57(4), pages 1056-1083.
    16. Dimitrios Kydros & Maria Argyropoulou & Vasiliki Vrana, 2021. "A Content and Sentiment Analysis of Greek Tweets during the Pandemic," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    17. Elif Günalan & Saadet Turhan & Betül Yıldırım Çavak & İrem Kaya Cebioğlu & Özge Çonak, 2022. "The Evaluation of Videos about Branched-Chain Amino Acids Supplements on YouTube ™ : A Multi-Approach Study," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    18. Robert Hogenraad, 2019. "Fear in the West: a sentiment analysis using a computer-readable “Fear Index”," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1239-1261, May.

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