Affiliations: [a] College of Computer Science & Technology, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China | [b] Beijing Beibian MicroGrid Technology Co., Ltd., Beijing 100193, China
Correspondence:
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Corresponding author: Jian Feng, College of Computer Science & Technology, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China. E-mail: [email protected].
Abstract: Hot topic identification from micro-blog is very important for detection and control of the public opinion. When using Single-pass algorithm to cluster hot topics for Chinese micro-blog, Chinese word segmentation technology is a necessary preprocessing, but it will introduce inevitable segment errors. This kind of errors will make topic identification has low clustering precision. To solve this problem, this paper proposed an improved algorithm based on Single-pass which combines CS (Cosine Similarity) and LCS (Longest Common Subsequences) to calculate the similarity between Chinese words. Experiments on three different micro-blog data sets for hot topic identification are made, and the results show that the improved algorithm has both higher recall rate and precision rate than the original ones. The proposed algorithm is feasible and effective.
Keywords: Hot topic identification, clustering, Single-pass, word segmentation
