News bias analysis based on stakeholder mining

Abstract

In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods. © 2011 The Institute of Electronics, Information and Communication Engineers.