Abstract:

In order to solve problems which include the topic drift phenomenon and much higher level of noise in micro-blogs, an algorithm named the Streaming Dynamic Topic Model, which improves the dynamic topic model with MEntropy, was presented to track additional events on topics. The method of the dynamic topic model was  first tried to update the topic in the whole tracking process, which enhanced the description power of the topic model by both positive and negative sides to overcome the topic drift problem. However, as a high level of neutral posts existed, MEntropy was defined and used to evaluate the importance of a microblog for tracking a topic, and was then extended to the dynamic topic model in order to make   a better distinction between even microblogs and neutral ones. Topic tracking experiments on a collection of more than 170,000 users’ 12 million microblogs show that our algorithm is more efficient and with lower noise compared with the traditional dynamic topic model.

Key words: topic tracking; topic drift; dynamic topic model; MEntropy