A framework for semantic connection based topic evolution with DeepWalk

One of the prevalent studies on Topic Detection and Tracking (TDT) is topic evolution. With the emergence of Internet data, there is a clear need for intuitive and adaptive methods to analyze a series of evolutions. Most approaches completely depend on topic models and only focus on whether topics are changed while ignoring the degree of changes, resulting in poor quality topics and insensitivity of changes. In this paper, we propose a framework of topic evolution based on semantic connections which not only indicates the content similarity between documents but also shows the time decay for an adaptive number of topics and rapid responses to the changes of contents. Additionally, semantic connection features can be used to visualize topic evolution, which makes the analyses much easier. For empirical studies, three data sets in real applications are chosen to prove the effectiveness of our method, and the results show that our method has a better performance in reducing redundant topics, avoiding topic suppression, and discerning the vanishment of old topics and the appearance of new topics.

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