KANSHIN: A Cross-Lingual Concern Analysis System Using Multilingual Blog Articles

An architecture of cross-lingual concern analysis (CLCA) using multilingual blog articles, and its prototype system are described. As various people who are living in various countries use the Web, cross-lingual information retrieval (CLIR) plays an important role in the next generation search. In this paper, we propose a CLCA as one of CLIR applications for facilitating users to find concerns of people across languages. We propose a layer architecture of CLCA, and its prototype system called KANSHIN. The system collects Japanese, Chinese, Korean, and English blog articles, and analyzes concerns across languages. Users can find concerns from several viewpoints such as temporal, geographical, and a network of blog sites. The system also facilitates users to browse multilingual keywords using Wikipedia, and the system facilitates users to find spam blogs. An overview of the CLCA architecture and the system are described.