TWEET ANALYSIS FOR REAL-TIME EVENT DETECTION AND EARTHQUAKE REPORTING SYSTEM

The real time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to target events. To detect a target event, devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. Here regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimation the location of target events. As an application, developed an earthquake reporting system for use in japan. Because of the numerous earthquakes and the large number of twitter users throughout the country, propose an event detection algorithm, which helps to detect new events, analyze the spatial and temporal pattern of an event, and to identify importance of events. In this demonstration, the overall system components that crawl, classify, and rank tweets and extract locations from tweets, and present some interesting results of our system.