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Validating Cross-Perspective Topic Modeling for Extracting Political Parties' Positions from Parliamentary Proceedings

Authors :
van der Zwaan, J.M.
Marx, M.
Kamps, J.
Kaminka, G.A.
Fox, M.
Bouquet, P.
Hüllermeyer, E.
Dignum, V.
Dignum, F.
van Harmelen, F.
Information and Language Processing Syst (IVI, FNWI)
ILLC (FGw)
Cultural Heritage and Identity
Faculteit der Geesteswetenschappen
Language and Computation (ILLC, FNWI/FGw)
Source :
ECAI 2016 : 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands: including Prestigious applications of intelligent systems (PAIS 2016) : proceedings, 28-36, STARTPAGE=28;ENDPAGE=36;TITLE=ECAI 2016 : 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands
Publication Year :
2016
Publisher :
IOS Press, 2016.

Abstract

In the literature, different topic models have been introduced that target the task of viewpoint extraction. Because, generally, these studies do not present thorough validations of the models they introduce, it is not clear in advance which topic modeling technique will work best for our use case of extracting viewpoints of political parties from parliamentary proceedings. We argue that the usefulness of methods like topic modeling depend on whether they yield valid and reliable results on real world data. This means that there is a need for validation studies. In this paper, we present such a study for an existing topic model for viewpoint extraction called cross-perspective topic modeling [11]. The model is applied to Dutch parliamentary proceedings, and the resulting topics and opinions are validated using external data. The results of our validation show that the model yields valid topics (content and criterion validity), and opinions with content validity. We conclude that cross-perspective topic modeling is a promising technique for extracting political parties' positions from parliamentary proceedings. Second, by exploring a number of validation methods, we demonstrate that validating topic models is feasible, even without extensive domain knowledge.

Details

Language :
English
Database :
OpenAIRE
Journal :
ECAI 2016 : 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands: including Prestigious applications of intelligent systems (PAIS 2016) : proceedings, 28-36, STARTPAGE=28;ENDPAGE=36;TITLE=ECAI 2016 : 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands
Accession number :
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