Synergistic Diverse Perspective for Topic Evolution Analysis on Weibo

Nowadays, Weibo has been one of the most popular social media platforms for information dissemination, social interaction, and public opinion influence. It is an urgent study for grasping the dynamic evolution of public opinion on Weibo. However, most of existing studies take little account of the evolution of local topics instead merely discussed the overall distribution of topics along the timeline as well as the low quality of generated topics due to data sparsity. In this paper, we exploit an innovative method to extract the topic vectors from Weibo by fusing multi-semantic vectors denotation. The extracted vectors generate a new topic representations from both the global and the local perspectives to enhance the semantic depth of the topic descriptions. Extensive experimental results show that the effectiveness of our proposed method that can comprehensively mine the potential evolutionary information on Weibo.