A Tweet Visualization System for Composite Facilities based on Spatio-Temporal Analysis of Geo-Tagged Tweets

Abstract

Twitter evidently stirred a popular trend of personal update sharing. Twitter users can be kept up to date with current information from Twitter; however, users cannot obtain the most recent information, while they browse web pages since these are not updated in real time. Meanwhile, there are many events happen at any time such as crowded restaurants and time sales in different floors or areas at composite facilities in urban areas. To solve them, it is thought that an appropriate method is to detect tweets of small-scale facilities at a composite facility to enrich their traditional web pages. Therefore, we developed a tweet visualization system to support users grasp event happens over time and space from tweets while they browse any web pages based on spatio-temporal analysis of tweets. In order to detect and analyze tweets of a composite facility, the system maps geo-tagged tweets to web pages by matching their location names, and classifies the tweets into different categories of small-scale facilities by utilizing machine learning algorithms. Thus, the system can visualize tweets in a tag cloud is associated with a web page to help users immediately gain a quick overview of events through space and time while they browse this web page, and it can also effectively present a list of most related tweets to help users obtain more detailed information about events. In this paper, we discuss our spatio-temporal analysis method and we have also included an evaluation of tweet classification into small-scale facilities and tag cloud generation that feature words of tweets are changed over time.