Fair News Reader: Recommending News Articles with Different Sentiments Based on User Preference
Fair News Reader: Recommending News Articles with Different Sentiments Based on User Preference
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DOI：
10.1007/978-3-540-74819-9_76
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Yukiko Kawai;Tadahiko Kumamoto;Katsumi Tanaka
中科院分区：
文献类型：
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作者：
Yukiko Kawai;Tadahiko Kumamoto;Katsumi Tanaka
我们开发了一个名为Fair News Reader（FNR）的新闻门户网站，该网站为用户推荐具有不同情感的新闻文章，这些文章涉及用户感兴趣的每个主题。FNR可以检测新闻文章的各种情感，并基于用户先前阅读的文章的情感来确定用户的情感偏好。虽然网络上有许多新闻门户网站，如GoogleNews，Yahoo！和MSN新闻，他们不能基于他们可能创建的情绪来推荐和呈现新闻文章，因为他们仅仅基于文章是否包含用户指定的关键字来选择文章。FNR根据用户感兴趣的主题以及文章可能产生的情绪来收集和推荐新闻文章。每篇文章可能产生的八种情绪由一个包含四个元素的”文章向量”表示。每个元素对应于由两个对称情感组成的度量。然后，提取先前阅读的关于主题的文章的情感，并表示为“用户向量”。最后，基于每个主题中的用户向量和文章向量之间的比较，FNR推荐具有与用户阅读文章的情感对称的情感的文章，以公平地阅读该主题。使用两个实验的FNR的评估表明，用户向量可以确定FNR的基础上的情绪的阅读文章关于一个主题，它可以提供一个独特的接口与类别包含推荐的文章。
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.