The purpose of this research is supporting information access based on the contents of comic books. To meet this purpose, it is necessary to obtain information related to the story and the characters of a comic. We propose a method to extract information from reviews on the Web by using term frequency-inversed document frequency (TFIDF) method and hierarchical Latent Dirichlet Allocation (hLDA) method, which intends to solve the problem. By using these methods, we build a prototype system for exploratory comic search. We conducted a user study to observe how a participant use the system. The user study showed that the system successfully supported the participants to find interesting unread comics.