Dynamic and Static Topic Model for Analyzing Time-Series Document Collections
This work addresses the need for better topic modeling in structured time-series documents like news articles or scientific papers, though it appears incremental as it builds on existing topic modeling approaches.
The authors tackled the problem of extracting meaningful topics from time-series document collections by proposing a dynamic and static topic model that accounts for temporal evolution and hierarchical structures, and they reported that their method outperformed conventional models in experiments on scientific papers.
For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.