Leveraging Meta Information in Short Text Aggregation
He Zhao, Lan Du, Guanfeng Liu, Wray Buntine · 2019
Analysing topics in short texts (e.g., tweets and new headings) is a challenging task because short texts often contain insufficient word co-occurrence information, which is important to learn good topics in conventional topic topics.To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information.Our model can generate more interpretable topics as well as document clusters.We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model.Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.