Twitter is a widely used micro-blogging service, which in recent times, has become a reliable source of happening news around the world [11]. 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 large 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. In this work, we attempt to automatically detect significant events related to an entity. An episode, is an event of importance; identified by processing the volumes of tweets/posts in a short time. The key features implemented in Tweet Episode Analytics (TEA) system are: (i) detecting episodes among the streaming tweets related to a given entity over a period of time (from the entity’s birth i.e., mention in the tweet world till date), (ii) providing visual analytics (like sentiment scoring and frequency of tweets over time) of each episode through graphical interpretation.
['Prapula G', 'Soujanya Lanka', 'Kamalakar Karlapalem']
TEA: Episode Analytics on Short Messages