An Online Topic Modeling Framework with Topics Automatically Labeled
This is an incremental improvement for researchers analyzing topic evolution in online forums.
The authors tackled the problem of tracking and labeling topic changes in deep learning discussions on Stack Exchange by proposing the IEDL framework, which achieved effectiveness in experiments on 7,076 posts.
In this paper, we propose a novel online topic tracking framework, named IEDL, for tracking the topic changes related to deep learning techniques on Stack Exchange and automatically interpreting each identified topic. The proposed framework combines the prior topic distributions in a time window during inferring the topics in current time slice, and introduces a new ranking scheme to select most representative phrases and sentences for the inferred topics in each time slice. Experiments on 7,076 Stack Exchange posts show the effectiveness of IEDL in tracking topic changes and labeling topics.