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
- ,
- ,
- ,
- 巻
- 2018-
- 号
- 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
- ID情報
-
- DOI : 10.1109/BigData.2017.8258238
- DBLP ID : conf/bigdataconf/HashimotoOKS17
- SCOPUS ID : 85047730612