Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions
This work addresses the challenge of analyzing large-scale online discussions for social media platforms, though it is incremental in extending existing topic modeling approaches.
The authors tackled the problem of topic modeling for online discussions by incorporating thread structure, popularity, and transitivity into a new model, achieving improved topic extraction and assignment accuracy as demonstrated on real forum datasets.
Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a "popularity" metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the "transitivity" concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.