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2020
Conference Paper
Titel

TopicsRanksDC: Distance-based topic ranking applied on two-class data

Abstract
In this paper, we introduce a novel approach named TopicsRanksDC for topics ranking based on the distance between two clusters that are generated by each topic. We assume that our data consists of text documents that are associated with two-classes. Our approach ranks each topic contained in these text documents by its significance for separating the two-classes. Firstly, the algorithm detects topics using Latent Dirichlet Allocation (LDA). The words defining each topic are represented as two clusters, where each one is associated with one of the classes. We compute four distance metrics, Single Linkage, Complete Linkage, Average Linkage and distance between the centroid. We compare the results of LDA topics and random topics. The results show that the rank for LDA topics is much higher than random topics. The results of TopicsRanksDC tool are promising for future work to enable search engines to suggest related topics.
Author(s)
Yousef, Malik
Zefat Academic College, Zefat, Israel / The Galilee Digital Health Research Center (GDH), Zefat, Israel
Al Qundus, Jamal
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Peikert, Silvio
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Paschke, Adrian
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Hauptwerk
Database and Expert Systems Applications. DEXA 2020 International Workshops. Proceedings
Project(s)
Qurator
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Database and Expert Systems Applications (DEXA) 2020
International Workshop on Biological Knowledge Discovery from Data (BIOKDD) 2020
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DOI
10.1007/978-3-030-59028-4_2
Language
English
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