Named Entity Oriented Difference Analysis of News Articles and Its Application

Abstract

To support the efficient gathering of diverse information about a news event, we focus on descriptions of named entities (persons, organizations, locations) in news articles. We extend the stakeholder mining proposed by Ogawa et al. and extract descriptions of named entities in articles. We propose three measures (difference in opinion, difference in details, and difference in factor coverage) to rank news articles on the basis of analyzing differences in descriptions of named entities. On the basis of these three measurements, we develop a news app on mobile devices to help users to acquire diverse reports for improving their understanding of the news. For the current article a user is reading, the proposed news app will rank and provide its related articles from different perspectives by the three ranking measurements. One of the notable features of our system is to consider the access history to provide the related news articles. In other words, we propose a context-aware re-ranking method for enhancing the diversity of news reports presented to users. We evaluate our three measurements and the re-ranking method with a crowdsourcing experiment and a user study, respectively.