  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.
