Extraction of Reliable Reputation Information Using Contributor´s Stance

This paper describes a method for extracting reliable reputation on the Web. In this research, reliable reputation is the information that has an opposite polarity value of contributor\´s stance (positive or negative). We call this information "fair reputation". In order to extract fair reputations, we develop the following two tasks. The first task is classification of feedback documents into positive or negative classes. For this task, we propose a classification method using \´document level reputation\´ that can determine the polarity value of the document. The second task is extraction and classification of reputations. Using the classified reputations, we extract "fair reputations". The experimental results using movie reviews showed that the proposed method could classify feedback documents more correctly than the previous method, and that fair reputations are useful for evaluating reputations