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Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data

Authors Andreas Baumann , Klaus Hofmann, Bettina Kern , Anna Marakasova, Julia Neidhardt , Tanja Wissik

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Author Details

Andreas Baumann
  • University of Vienna, Austria
Klaus Hofmann
  • University of Vienna, Austria
Bettina Kern
  • University of Vienna, Austria
Anna Marakasova
  • TU Wien, Austria
Julia Neidhardt
  • TU Wien, Austria
Tanja Wissik
  • Austrian Academy of Sciences, Vienna, Austria

Funding

This research was funded by the Austrian Academy of Sciences (go!digital Next Generation grant, GDNG 2018-020) and by the City of Vienna (Digitaler Humanismus grant, MA7-737909/19).

Cite As Get BibTex

Andreas Baumann, Klaus Hofmann, Bettina Kern, Anna Marakasova, Julia Neidhardt, and Tanja Wissik. Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 38:1-38:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/OASIcs.LDK.2021.38

Abstract

We explore relationships between dynamics of emotion (arousal and valence) and topical stability in political discourse in two diachronic corpora of Austrian German. In doing so, we assess interactions among emotional and topical dynamics related to political parties as well as interactions between two different domains of discourse: debates in the parliament and journalistic media. Methodologically, we employ unsupervised techniques, time-series clustering and Granger-causal modeling to detect potential interactions. We find that emotional and topical dynamics in the media are only rarely a reflex of dynamics in parliamentary discourse.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Lexical semantics
  • Computing methodologies → Discourse, dialogue and pragmatics
  • Information systems → Sentiment analysis
Keywords
  • time-series clustering
  • Granger causality
  • topical stability
  • emotion
  • political discourse

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