Development of a kansei engineering artificial intelligence sightseeing application

Abstract

Kansei engineering methods and text mining methods were applied to the web application for assisting sightseeing travel at Kure city. Kansei engineering methods used here were a questionnaire and multivariate analyses for research inter-relations between aims of travels and interests. Text mining was done on logged tweets on Twitter. The number of tweets mentioned on Kure was around 3400 to 3700 tweets per day. The text mining reveals latest events and people’s interests in the daily basis. With Twitter text mining to conventional Kansei engineering methods, both general Kansei on sightseeing and rapidly changing interests are kept reflecting to the inference rules of the web-application.