其他摘要：Under the policy of Society 5.0, all kinds of information and intellectual information across domains are extracted, exchanged, collaborated, shared, and reused. In particular, the SNS (Social Networking Service) has been used in a wide range of generations, researches on the data science has been actively conducted in fields. And then, the most important thing is the reliability, it is important to acquire attribute such as the gender, age, occupation, and region of the poster. The authors have been able to obtain certain results in the acquisition of three attributes other than the area attribute. However, it was difficult to estimate the locality because there were few characteristic words and the rate of location information was low. In the present research, we focus on characteristic words and words that express the same thing, and words that are used differently in each region. It is the dialects like “Kaitenyaki, Imagawayaki and Obanyaki”. Then, we propose a method to gradually extend the regional estimation model by automatically extracting users in the target region from the profile fields of SNS and learning them sequentially. In this way, we were able to estimate the data more accurately than existing methods.
