A Study of Topic Person Stance Analysis

With the explosive growth of the Internet, people can easily receive astronomical information from the Web, and could be overwhelming by the online medium, e.g. news, review comments, forum posts or information from the social medium. For facilitating the people digest the enormous information, we investigate a novel problem named “topic person stance identification,” which is to identify the stances of the topic persons from topic documents, in this dissertation. We proposed two methodologies to copy with the problem. First, we proposed a methodology named model-based EM method to identify the stances of the topic persons by leveraging the pattern of person name co-occurrence in the documents. In addition, the level of co-occurrence and non-co-occurrence of the person names in the documents are considered to weight the pattern of the person name co-occurrence. Moreover, we developed an initialization algorithm to stable the results of identifying the stance communities because the EM method is sensitive to the initialization. The second methodology is called stance community identification of topic persons using friendship network analysis. This method is to take the friendly (opposing) orientation of the documents into consideration to construct the friendship network automatically from the topic documents. For identifying the stance community, we proposed stance community expansion and stance community refinement algorithms to identify the stance communities based on the network. The experimental results of two methodologies demonstrated our methods are outperformed other well-known clustering approach, and can effectively identify the stances of the topic persons.