Topic Tracking and Visualization Method using Independent Topic Analysis presented at CNSA 2020

by Takahiro Nishigaki, Kenta Yamamoto, And Takashi Onoda,

Summary : In this paper propose a topic tracking and visualization method using Independent Topic Analysis. Independent Topic Analysis is a method for extracting mutually independent topics from the documents data by using the Independent Component Analysis. In recent years, as the amount of information increases, there is often a desire to analyse topic transitions in time-series documents and track topics. For example, it is possible to analyse the causes of trend and hoaxes by SNS and predict future changes. However, there is no topic tracking method in Independent Topic Analysis. There is also no way to visualize topic tracking. So, topics in each period was extracted, and topic transition was analysed based on the similarity of topics. And, a method for tracking these four topics was proposed. In addition, this paper developed an interface that visualizes time-series changes of the tracked topics and obtained effective results through user experiments.