Abstract：Timely acquiring of hot topics is of great significance for commercial innovation and business marketing. Existing methods mostly need to cope with non-structured data or repeated traversal sample set, which results in high complexity. In this paper, emphasizing the topic statistical properties, a non-parameter method based on structured data is proposed to acquire the hot topics in time. Firstly, diffusion degree and focus degree are introduced to build heat curves to characterize the topics. Then, the varied heat curves are classified to determine the common behaviors of the topics. Finally, the weighted-vote scheme is employed to predict whether a topic is trend or not. The experimental results on Sina microblog show that the proposed method has simple data structure and works well with low time complexity and simple manipulation.
刘业政，杜亚楠，姜元春，杜非. 基于热度曲线分类建模的微博热门话题预测*[J]. 模式识别与人工智能, 2015, 28(1): 27-34.
LIU Ye-Zheng, DU Ya-Nan, JIANG Yuan-Chun, DU Fei. Trend Prediction for Microblog Based on Classification Modeling of Heat Curves. , 2015, 28(1): 27-34.
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2015, Vol. 28 
