Episode Analytics


Twitter is a widely used micro-blogging service, which in recent times, has become a reliable source of happening news around the world. Breaking news are covered in twitter; the magnitude and volumes of tweets reflecting on the nature and intensity of the news. During events, many tweets are posted either expressing sentiments about the event or just about the occurrence of the event. Events related to an entity that have attracted a huge number of tweets can be considered significant in the entity's twitter lifetime. Entity could represent a person, movie, community, electronic gadgets, software products and like wise. These significant events collected together could be the potential biography of entity. In this work, we attempt to automatically detect significant events to compile entity's biography. An episode, is an event of importance; identified by observing the volumes of tweets/posts in a short period. The key features implemented in this TEA (Tweet Episode Analytics) system are:

  • detecting an episode among the streaming tweets,
  • providing visual analytics (like sentiment scoring and frequency of tweets over time) of each episode through graphical interpretation
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