As one of the prevalent topic mining methods, neural modeling has attracted a lot interests due to advantages low training costs and strong generalisation abilities. However, existing models may suffer from feature sparsity problem when applied short texts, lack context in each message. To alleviate this issue, we propose Context Reinforced Neural Topic Model (CRNTM), whose characteristics can ...

Abstract The past few decades have seen the rapid development of topic modeling. So far, research has been more concerned with determining ideal number topics or meaningful clustering words than applying modeling techniques to evaluate linguistic theories. This study proposes Structural Topic Model (STM)-led framework facilitate interpretation results and standardize text analysis. STM encompas...