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

Aritz Bilbao-Jayo and Aitor Almeida

International Journal of Distributed Sensor Networks, 2018, vol. 14, issue 11, 1550147718811827

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.

Keywords: Supervised classification; convolutional neural networks; online political discourse; sentence classification (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:14:y:2018:i:11:p:1550147718811827

DOI: 10.1177/1550147718811827

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