Transitive Topic Modeling with Conversational Structure Context: Discovering Topics that are Most Popular in Online Discussions (original) (raw)

International Journal of Semantic Computing, 2020

Abstract

With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. 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.

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