Detecting Events from Official Weibo Profiles Based on Post Clustering with Burst Words
Gao Yongbing1(), Yang Guipeng1, Zhang Di1, Ma Zhanfei2
1School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China 2Department of Computer, Baotou Teachers’ College, Baotou 014010, China;
[Objective] This paper aims to remove the unrelated information from the official Weibo (micro-blog) profiles, and then retrieves the posts on official events. [Methods] First, we used the word2vec machine learning model to train the official Weibo datasets. Then, we proposed an official micro burst words detection method based on the influence of Weibo posts, the base weight and the related official profiles. Third, we calculated the similarity of blog posts with the burst words, and used hierarchical clustering algorithm to select burst words for the target events. [Results] The proposed algorithm had better precision (63.5%), recall (85.5%) and F values (0.73) than the traditional TF-IDF and TextRank algorithms. [Limitations] The official profiles did not have enough historical data on the events. [Conclusions] The burst words help us detect official events effectively from the official Weibo profiles.
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Gao Yongbing,Yang Guipeng,Zhang Di,Ma Zhanfei. Detecting Events from Official Weibo Profiles Based on Post Clustering with Burst Words. Data Analysis and Knowledge Discovery, 2017, 1(9): 57-64.
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