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Sentiment-based and hashtag-based Chinese online bursty event detection

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Abstract

How to detect bursty events in data streams on social media is a hot research topic in natural language processing. However, current methods for extracting bursty events suffer from poor accuracy and low efficiency. Fortunately, sentiment analysis has been applied to event detection, which has improved the performance greatly. Inspired by this, this paper proposes a new model which utilizes sentiment analysis for Chinese bursty event detection. First, we build a sentiment co-occurrence graph offline and apply it to analyze microblog sentiment. Plutchik’s emotion wheel is the base for the sentiment classification of the graph. Second, sentiment is used as features to detect bursts in microblog streams online. At last, we exploit regular expressions to extract hashtags in bursty periods and segment hashtags into keywords. By using mutual information and frequent patterns, we fetch words relevant to hashtags as keywords to form events. This approach can detect bursty events online while analyzing the sentiment of microblogs. The experimental results on a large real dataset show that our method can detect bursty events with higher accuracy in a shorter time than traditional methods.

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  1. http://www.techweb.com.cn/data/2016-05-12/2331298.shtml

  2. http://www.keenage.com/

  3. http://www.nlpir.org/?action-viewnews-itemid-263

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Acknowledgements

This paper is supported by (1) the National Natural Science Foundation of China under Grant nos. 61672179, 61370083 and 61402126, (2) Research Fund for the Doctoral Program of Higher Education of China under Grant nos. 20122304110012, (3) the Youth Science Foundation of Heilongjiang Province of China under Grant no. QC2016083, (4) Heilongjiang postdoctoral Fund no. LBH-Z14071. This paper is also supported by China Scholarship Council.

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  1. College of Computer Science and Technology, Harbin Engineering University, Heilongjiang, 150001, China

    Zou Xiaomei, Yang Jing & Zhang Jianpei

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  1. Zou Xiaomei
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  2. Yang Jing
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  3. Zhang Jianpei
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Correspondence to Yang Jing.

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Xiaomei, Z., Jing, Y. & Jianpei, Z. Sentiment-based and hashtag-based Chinese online bursty event detection. Multimed Tools Appl 77, 21725–21750 (2018). https://doi.org/10.1007/s11042-017-5531-y

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  • DOI: https://doi.org/10.1007/s11042-017-5531-y

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