Why Are They Excited? Identifying and Explaining Spikes in Blog Mood Levels

We describe a method for discovering irregularities in temporal mood patterns appearing in a large corpus of blog posts, and labeling them with a natural language explanation. Simple techniques based on comparing corpus frequencies, coupled with large quantities of data, are shown to be effective for identifying the events underlying changes in global moods.

[1]  Gilad Mishne,et al.  Capturing Global Mood Levels using Blog Posts , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[2]  Ravi Kumar,et al.  On the Bursty Evolution of Blogspace , 2003, WWW '03.

[3]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[4]  Gilad Mishne,et al.  MoodViews: Tools for Blog Mood Analysis , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[5]  Janyce Wiebe,et al.  Computing Attitude and Affect in Text: Theory and Applications , 2005, The Information Retrieval Series.

[6]  G. A. Mishne,et al.  Expiriments with mood classification in blog posts , 2005, SIGIR 2005.

[7]  Yasuhiro Suzuki,et al.  Automatically collecting, monitoring, and mining japanese weblogs , 2004, WWW Alt. '04.

[8]  Ted Dunning,et al.  Accurate Methods for the Statistics of Surprise and Coincidence , 1993, CL.