Document-based Topic Coherence Measures for News Media Text

Korenčić, Damir; Ristov, Strahil; Šnajder, Jan (2018) Document-based Topic Coherence Measures for News Media Text. Expert Systems with Applications, 114 . pp. 357-373. ISSN 0957-4174

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Abstract

There is a rising need for automated analysis of news text, and topic models have proven to be useful tools for this task. However, as the quality of the topics induced by topic models greatly varies, much research effort has been devoted to their automated evaluation. Recent research has focused on topic coherence as a measure of a topic’s quality. Existing topic coherence measures work by considering the semantic similarity of topic words. This makes them unfit to detect the coherence of transient topics with semantically unrelated topic words, which abound in news media texts. In this paper, we intro- duce the notion of document-based topic coherence and propose novel topic coherence measures that estimate topic coherence based on topic documents rather than topic words. We evaluate the proposed measures on two datasets containing topics manually labeled for document-based coherence, on which the proposed measures outperform a strong baseline as well as word-based coherence measures. We also demonstrate the usefulness of document-based coherence measures for automated topic discovery from news media texts.

Item Type:	Article
Uncontrolled Keywords:	topic models; topic coherence; topic model evaluation; text analysis; news text; exploratory analysis
Subjects:	TECHNICAL SCIENCES > Computing > Artificial Intelligence
Divisions:	Division of Electronics
Depositing User:	Damir Korenčić
Date Deposited:	28 Aug 2018 12:03
Last Modified:	30 Dec 2020 00:15
URI:	http://fulir.irb.hr/id/eprint/4124
DOI:	10.1016/j.eswa.2018.07.063

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