Document-based topic coherence measures for news media text

• Published in: Expert systems with applications 2018-12, Vol.114, p.357-373
• Main Authors: Korenčić, Damir, Ristov, Strahil, Šnajder, Jan
• Format: Article
• Language: English
• Subjects: Coherence; Datasets; Exploratory analysis; Influence; News media; News text; Text analysis; Texts; Topic coherence; Topic model evaluation; Topic models
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.07.063

•Novel class of document-based coherence measures for news topics proposed and evaluated.•High-performing document-based coherence measure identified.•Document-based coherence measures contrasted with state-of-art word-based measures.•Application of document-based measures for semi-automated topic discovery. 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 introduce 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.