論文

査読有り
2018年1月12日

Topic life cycle extraction from big Twitter data based on community detection in bipartite networks

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
  • Takako Hashimoto
  • ,
  • Hiroshi Okamoto
  • ,
  • Tetsuji Kuboyama
  • ,
  • Kilho Shin

2018-
開始ページ
2740
終了ページ
2745
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/BigData.2017.8258238
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

This paper is showing a time series topic life cycle extraction from millions of Tweets using our original community detection technique in bipartite networks. We suppose that the authors role that means who belong to what topics is important to extract quality topics from social media data. We already proposed the topic extraction method that considers the relationship between the authors and the words as bipartite networks and explores the authors role by forming clusters as topics. As the next step, this paper applies our method to the time series topic life cycle detection. We extract topics in different time slots and analyze the time series of topic transition using the coherence measure that expresses the semantic accuracy of topics. The paper demonstrates that our method can detect the topic life cycle such as the growth, the conflicts and so on over time from millions of Tweets.

リンク情報
DOI
https://doi.org/10.1109/BigData.2017.8258238
URL
http://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2017.html#conf/bigdataconf/HashimotoOKS17
ID情報
  • DOI : 10.1109/BigData.2017.8258238
  • DBLP ID : conf/bigdataconf/HashimotoOKS17
  • SCOPUS ID : 85047730612

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