Fair News Reader: Recommending News Articles with Different Sentiments Based on User Preference

We have developed a news portal site called Fair News Reader (FNR) that recommends news articles with different sentiments for a user in each of the topics in which the user is interested. FNR can detect various sentiments of news articles, and determine the sentimetal preferences of a user based on the sentiments of previously read articles by the user. While there are many news portal sites on the Web, such as GoogleNews, Yahoo!, and MSN News, they can not recommend and present news articles based on the sentiments they are likely to create since they simply select articles based on whether they contain user-specified keywords. FNR collects and recommends news articles based on the topics in which the user is interested and the sentiments the articles are likely to create. Eight of the sentiments each article is likely to create are represented by an "article vector" with four elements. Each element corresponds to a measure consisting of two symmetrical sentiments. The sentiments of the articles previously read with respect to a topic are then extracted and represented as a "user vector". Finally, based on a comparison between the user and article vectors in each topic, FNR recommends articles that have symmetric sentiments against the sentiments of read articles by the user for fair reading about the topic. Evaluation of FNR using two experiments showed that the user vectors can be determined by FNR based on the sentiments of the read articles about a topic and that it can provide a unique interface with categories containing the recommended articles.