Understanding Document Thematic Structure

Abstract

The increasing usage of the Internet and other digital platforms has brought in the eraof big data with the attending increase in the quantity of unstructured data that isavailable for processing and storage. However, the full benefits of analyzing this largequantity of unstructured data will not be realized without proper techniques andalgorithms. Topic modeling algorithms have seen a major success in this area. Differenttopic modeling algorithms exist and each one either employs probabilistic or linearalgebra approaches. Recent reviews on topic modeling algorithms dwell majorly onprobabilistic methods without giving proper treatment to the linear-algebra-basedalgorithms. This review explores linear-algebra-based topic models as well asprobability-based topic models. An overview of how models generated by each of thesealgorithms represent document thematic structure is also presented