2018 Volume 30 Issue 6 Pages 804-814
To invite the tourist effectively, it is essential to place an advertisement in the cities where many citizens have visited the leisure venue for sightseeing. The mood of the citizens’ concerns for the cities or for the districts are different according to the cities, and the reason of the concerns is also different with their purposes as homecoming or sightseeing. In this paper, we propose a method to visualize the mood of the citizen concerns for the regional names (city names, district names, etc.) with clustering the similar objects using the Twitter data crawled for each city. At first, we collect the citizen users in Twitter using profiles to crawl the citizen users’ tweets and also select the tweets including regional names and movement verbs to regions. Then, we create distributed vector representation of words based on skip-gram using the selected tweets. We also choose two terms pair to compare the mood of citizen concerns for regional names based on movement intention and factuality. The regional names are visualized based on the difference of the normalized similarities of the regional names between two chosen terms pair using Z-score. They are classified using clustering technique to clarify the similar concerns of citizens for regional names. To verify the effectiveness of the proposed method, we conducted the experiment using Twitter users in eight cities in Japan. We confirmed that the popular sightseeing spot, residence area, and homecoming spot were visualized properly and the cities sharing tourism purposes or in short distance area tended to be classified in the same cluster.
