EmoTrend: Emotion Trends for Events

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• 作者：Yi-Shin Chen (17)
  Carlos Argueta (17)
  Chun-Hao Chang (17)
  
  17. Intelligent Data Engineering and Applications Laboratory ; Institute of Information Systems and Applications ; National Tsing Hua University ; No. 101 ; Section 2 ; Kuang-Fu Road ; Hsinchu ; 30013 ; Taiwan ; Republic of China
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9050
• 期：1
• 页码：522-525
• 全文大小：265 KB
• 参考文献：1. Porter, M.F.: An algorithm for suffix stripping. Program: Electronic Library and Information Systems 14(3), 130鈥?37 (1980)
  2. Page, L., et al.: The PageRank citation ranking: Bringing order to the web (1999)
  3. Kwan, E., et al.: Event identification for social streams using keyword-based evolving graph sequences. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM (2013); APA
  4. Pennebaker, J.W., et al.: The development and psychometric properties of LIWC 2007, Austin, TX, LIWC. Net (2007)
  5. Tausczik, YR, Pennebaker, JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29: pp. 24-54 CrossRef
  
• 作者单位：Database Systems for Advanced Applications
• 丛书名：978-3-319-18122-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

In this demo paper we present EmoTrend, a web-based system that supports event-centric temporal analytics of the global mood, as expressed in Twitter. Given a time range, and optionally a set of keywords, the system relies on peak frequencies, and the social graph, to identify relevant events. Subsequently, by performing sentiment analysis on related tweets, the global impact and reception of the events are presented by a visualization of the overall mood trend in the time range.