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.
This work has been partially supported by the “Wachstumskern Qurator – Corporate Smart Insights” project (03WKDA1F) funded by the German Federal Ministry of Education and Research (BMBF).
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Notes
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Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006).
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Yousef, M., Qundus, J.A., Peikert, S., Paschke, A. (2020). TopicsRanksDC: Distance-Based Topic Ranking Applied on Two-Class Data. In: Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2020. Communications in Computer and Information Science, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-59028-4_2
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