Abstract
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.
Information
Book title
人工知能学会論文誌
Volume
32
Pages
WII-D_1-11
Date of issue
2017/01/06
Date of presentation
2017/01/20
Citation
山下 諒, 朴 炳宣, 松下 光範. コミックの内容情報に基づいた探索的な情報アクセスの支援, 人工知能学会論文誌, Vol.32, No.1, pp.WII-D_1-11, 2017.