Topic detection as a tool to detect topics from online media attracts much attention. Generally, a topic is characterized by a set of informative keywords/terms. Traditional approaches are usually based on various topic models, such as Latent Dirichlet Allocation (LDA). They cluster terms into a topic by mining semantic relations between terms. However, co-occurrence relations across the document are commonly neglected, which leads to the detection of incomplete information. Furthermore, the inability to discover latent co-occurrence relations via the context or other bridge terms prevents the important but rare topics from being detected. To tackle this issue, we propose a hybrid relations analysis approach to integrate semantic relations and co-occurrence relations for topic detection. Specifically, the approach fuses multiple relations into a term graph and detects topics from the graph using a graph analytical method. It can not only detect topics more effectively by combing mutually complementary relations, but also mine important rare topics by leveraging latent co-occurrence relations. Extensive experiments demonstrate the advantage of our approach over several benchmarks. 
A hybrid term–term relations analysis approach for topic detection