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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (9): 57-64    DOI: 10.11925/infotech.2096-3467.2017.09.06
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Detecting Events from Official Weibo Profiles Based on Post Clustering with Burst Words
Yongbing Gao1(),Guipeng Yang1,Di Zhang1,Zhanfei Ma2
1School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
2Department of Computer, Baotou Teachers’ College, Baotou 014010, China;
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[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.

Key wordsOfficial Micro-blog      Related Words      Burst Words      Official Microblog Events      Word2Vec     
Received: 05 April 2017      Published: 18 October 2017

Cite this article:

Yongbing Gao,Guipeng Yang,Di Zhang,Zhanfei Ma. 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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