Research on Chinese Micro-blog Bursty Topics Detection
Trs Information · 2013
Much attention is paid to mining bursty topics accurately and efficiently from micro-blog nowadays.In this paper,a set of burst terms are extracted by counting the term frequency,calculating the growth rate of the terms and using Term Frequency-Proportional Document Frequency(TF-PDF) algorithm to measure the weight.And then micro-blog texts are described with the burst terms.Analyzing the characteristic that bursty topics propagate in the platform of micro-blog,the authors filter the texts that do not contribute to detect bursty topics.The paper proposes a novel clustering strategy of Absolute Clustering to cluster the micro-blog texts.By figuring up the hot spot of the texts with weighted value of reply and retweet number,the top 5 texts are extracted as the result of burst topics detection.The experiments show that the precision is 92.60%,the recall is 85.51% and the F-measure is 0.89.Contrast with the traditional method,the validity of the proposed method is proved.