LDA+: An Extended LDA Model for Topic Hierarchy and Discovery

Abstract

The success of topic modeling algorithms depends on their ability to analyze, index and classify large text corpora. These algorithms could be classified into two groups where the first one is oriented to classify textual corpus according to their dominant topics such as LDA, LSA and PLSA which are the most known techniques. The second group is dedicated to extract the relationships among the generated topics like HLDA, PAM and CTM. However, each algorithm among these groups is dedicated to a single task and there is no technique that makes it possible to carry out several analyses on textual corpus at the same time. In order to cope with this problem, we propose here a new technique based on LDA topic modeling to automatically classify a large text corpora according to their relevant topics, discover new topics (sub-topics) based on the extracted ones and hierarchy the generated topics in order to analyse data more deeply. Experiments have been conducted to measure the performance of our solution compared to the existing techniques. The results obtained are more than satisfactory.