Abstract
In evaluating public works projects, it is necessary to evaluate their current condition quantitatively and qualitatively from diversified viewpoints based on the social situation. For the road projects, road traffic analysis is carried out to grasp the actual traffic condition quantitatively. If this analysis result reflects public opinions that are qualitative in detail, it can be expected that this will help to advance the evaluations of road projects. To collect qualitative public opinions, one of the effective measures is to utilize tweets from Twitter, which is a form of microblogs. However, since there are all sorts of tweets in circulation, it is difficult to appropriately extract the tweets that are useful in evaluating road projects. Moreover, even if a useful tweet is extracted, it is very hard to identify the location indicated by the tweet.Thus in this research, we devised a text mining method that combines road traffic data found on the Web with tweets from Twitter, and evaluated the feasibility of extracting public opinions related to road works projects and of estimating their locations.
