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Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data

Author

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  • Aritz Bilbao-Jayo
  • Aitor Almeida

Abstract

In this article, the authors propose a new approach to automate the analysis of the political discourse of the citizens and public servants, to allow public administrations to better react to their needs and claims. The tool presented in this article can be applied to the analysis of the underlying political themes in any type of text, in order to better understand the reasons behind it. To do so, the authors have built a discourse classifier using multi-scale convolutional neural networks in seven different languages: Spanish, Finnish, Danish, English, German, French, and Italian. Each of the language-specific discourse classifiers has been trained with sentences extracted from annotated parties’ election manifestos. The analysis proves that enhancing the multi-scale convolutional neural networks with context data improves the political analysis results.

Suggested Citation

  • Aritz Bilbao-Jayo & Aitor Almeida, 2018. "Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:11:p:1550147718811827
    DOI: 10.1177/1550147718811827
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    References listed on IDEAS

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    1. Volkens, Andrea, 2002. "Manifesto Coding Instructions (Second Revised Edition)," Discussion Papers, Research Unit: Institutions and Social Change FS III 02-201, WZB Berlin Social Science Center.
    2. Mikhaylov, Slava & Laver, Michael & Benoit, Kenneth R., 2012. "Coder Reliability and Misclassification in the Human Coding of Party Manifestos," Political Analysis, Cambridge University Press, vol. 20(1), pages 78-91, January.
    3. 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.
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    Cited by:

    1. Ruben Sánchez-Corcuera & Adrián Nuñez-Marcos & Jesus Sesma-Solance & Aritz Bilbao-Jayo & Rubén Mulero & Unai Zulaika & Gorka Azkune & Aitor Almeida, 2019. "Smart cities survey: Technologies, application domains and challenges for the cities of the future," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.

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