Topic and sentiment detection has been considered as an effective method to reveal the facts and sentiments in a massive volume of information. Existing works mainly focus on separate topic and sentiment extraction or static topic-sentiment associations, neglecting topic-sentiment dynamics and missing the opportunity to provide a in-depth analysis of online news. Actually, sentiment orientations are highly dependent on topic content and thus detecting topic-sentiment associations and their evolution over time is very important. This paper proposes a manifold learning-based model to explore the topic-sentiment associations and their evolution over time in the online news domain. The proposed model can visualize the hidden sentiment dynamics of topics in a low-dimensional space. Extensive experiments are conducted on online news crawled from the American Cable News Networks (CNN) website. The experimental results show that the proposed model outperforms the KL distance-based and the Similarity-based methods and improves the accuracy of topic classification by 12 % .

中文翻译：

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主题情绪随时间演变：在线新闻的流形学习模型

话题和情感检测被认为是揭示海量信息中的事实和情感的有效方法。现有的工作主要集中在单独的主题和情感提取或静态主题-情感关联，忽视主题-情感动态，错过了对在线新闻进行深入分析的机会。实际上，情感取向高度依赖于主题内容，因此检测主题-情感关联及其随时间的演变非常重要。本文提出了一种基于流形学习的模型，以探索在线新闻领域中的主题-情感关联及其随时间的演变。所提出的模型可以在低维空间中可视化主题的隐藏情感动态。对从美国有线电视新闻网 (CNN) 网站抓取的在线新闻进行了大量实验。实验结果表明，所提出的模型优于基于KL距离和基于相似性的方法，并将主题分类的准确率提高了12%。