Dynamic topic modeling via self-aggregation for short text streams

Abstract

Social networks such as Twitter, Facebook, and Sina microblogs have emerged as major sources for discovering and sharing the latest topics. Because social network topics change quickly, developing an effective method to model such topics is urgently needed. However, topic modeling is challenging due to the sparsity problem and the dynamic change of topics in microblog streams. In this study, we propose dynamic topic modeling via a self-aggregation method (SADTM) that can capture the time-varying aspect of topic distributions and resolve the sparsity problem. The SADTM aggregates the observable and unordered short texts as the aggregated document without leveraging an external context to overcome the sparsity problem of short text. Furthermore, we exploit word pairs instead of words for each microblog to generate more word co-occurrence patterns. The SADTM models temporal dynamics by using the topic distribution at previous time steps in microblog streams to infer the current topic from sequential data. Extensive experiments on a real-world Sina microblog dataset demonstrate that our SADTM algorithm outperforms several state-of-the-art methods in topic coherence and cluster quality. Additionally, when applied in a search scene, our SADTM significantly outperforms all baseline methods in terms of the quality of the search results.