Hierarchical topic modeling based on the combination of formal concept analysis and singular value decomposition

Abstract

One of the ways to describe the content of internet sources is known as topic modeling, which tries to uncover the hidden thematic structures in document collections. Topic modeling applied to social networks can be useful for analysis in case of crisis situations, elections, launching a new product on the market etc. It becomes popular research area in recent years and represents the methods to browse, search and summarize large amount of the textual data. The main aim of this paper is to describe a new way for topic modeling based on the usage of Formal Concept Analysis combined with reduction by Singular Value Decomposition of the input data matrix. In difference to other common used method for topic modeling our proposed method is able to generate topic hierarchy, which offer more detail analysis of topics within the collection. Our approach is experimentally tested on the selected dataset of Twitter network contributions.