Trending Sentiment-Topic Detection on Twitter

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• 作者：Baolin Peng (14) (15)
  Jing Li (14) (15)
  Junwen Chen (14) (15)
  Xu Han (14) (16)
  Ruifeng Xu (17)
  Kam-Fai Wong (14) (15) (16)
  
  14. Dept. of Systems Engineering & Engineering Management ; The Chinese University of Hong Kong ; Shatin ; N.T. ; Hong Kong
  15. MoE Key Laboratory of High Confidence Software Technologies ; Beijing ; China
  16. Shenzhen Research Institute ; The Chinese University of Hong Kong ; Hong Kong ; Hong Kong
  17. Harbin Institute of Technology ; Shenzhen Graduate School ; Shenzhen ; China
  
• 关键词：Twitter ; Online social network ; Trending topic detection ; Sentiment analysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9042
• 期：1
• 页码：66-77
• 全文大小：263 KB
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• 作者单位：Computational Linguistics and Intelligent Text Processing
• 丛书名：978-3-319-18116-5
• 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

Twitter plays a significant role in information diffusion and has evolved to an important information resource as well as news feed. People wonder and care about what is happening on Twitter and what news it is bringing to us every moment. However, with huge amount of data, it is impossible to tell what topic is trending on time manually, which makes real-time topic detection attractive and significant. Furthermore, Twitter provides a platform of opinion sharing and sentiment expression for events, news, products etc. Users intend to tell what they are really thinking about on Twitter thus makes Twitter a valuable source of opinions. Nevertheless, most works about trending topic detection fail to take sentiment into consideration. This work is based on a non-parametric supervised real-time trending topic detection model with sentimental feature. Experiment shows our model successfully detects trending sentimental topic in the shortest time. After a combination of multiple features, e.g. tweet volume and user volume, it demonstrates impressive effectiveness with 82.3% recall and surpasses all the competitors.