When making a hotel reservation on a private or business trip, users use accommodation reservation websites and use hotel reviews posted on websites as supporting information. While there are a variety of review comments posted on hotel reservation websites, comments differ in content, depending on the difference of users’ purposes, such as a stay in a business area or that in a leisure area. Although there is a reported method that extracts feature representations from review comments in business areas, it is necessary to verify whether feature representations corresponding to each area characteristic can be extracted in accordance with the difference of areas. In the present study, we first graphed impression comments using co-occurrence restrictions and dependency structures employed by the previously reported method and then extracted feature representations by clustering the graph. By applying this method to each of the business area and the leisure area, we extracted the feature representations and the cluster distribution ratios, both of which differ between the two areas. This enabled us to present the order of importance of the decision-making factors for users when they select hotel accommodations in each area.
