A Personalized Affect Response Model for Online News Articles
A Personalized Affect Response Model for Online News Articles
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发表时间：
2019
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通讯作者：
Kyohei Atarashi;A. Moriyama;S. Oyama;M. Kurihara
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作者：
Kyohei Atarashi;A. Moriyama;S. Oyama;M. Kurihara
随着智能手机的普及和Web服务的多样化，个人、公司和组织可以在任何地方发送和接收各种类型的信息。虽然信息是由许多类型的媒体提供的，但文章和帖子是最常见的类型：新闻文章，博客帖子，社交媒体帖子等，这些文章和帖子可以在读者中产生意想不到的情绪反应，在最坏的情况下可以发生火焰战争。为了避免发生口水战的风险，并告知收件人的意图，个性化的情感分析的方法已经引起了人们的注意。本文提出了一个基于潜在狄利克雷分配的新闻文章个性化情感分析模型。假设每篇文章都有一个主题分布，并且假设每个读者都有潜在的特征，这些特征代表了每个主题对读者影响的强度。读者对文章的反应是基于文章所包含的主题的分布和读者的潜在特征。此外，该模型利用没有读者响应的文章进行训练。通过众包收集的读者对几篇在线新闻文章的反应数据证明了所提出的模型的有效性。
With the spread of smartphones and the diversification of Web services, individuals, companies, and organizations can send and receive various types of information to and from anywhere. Although information is provided by many types of media, articles and posts are the most common type: news articles, blog posts, social media posts, etc. Such articles and posts can create unexpected emotional responses in readers and can occur flame war in the worst case. To avoid the risk of occurrence of flame war and to inform the recipient as intended, methods for personalized affect analysis have attracted attention. We present a model based on latent Dirichlet allocation for performing personalized affect analysis of news articles. Each article is assumed to have a distribution of topics, and each reader is assumed to have latent features that represent the strength of the effect of each topic on the reader. A reader responds to an article on the basis of the distribution of the topics it contains and the reader’s latent features. Furthermore, the model leverages the articles to which no readers respond for training. The effectiveness of the proposed model was demonstrated using data on the responses, as collected through crowdsourcing, of readers to several online news articles.