论文标题
S-APIR：基于新闻的商业情感指数
S-APIR: News-based Business Sentiment Index
论文作者
论文摘要
本文介绍了我们使用日报文章开发新的商业情感指数的工作。我们采用带有封闭式复发单元的经常性神经网络（RNN）来预测给定文本的业务情感。最初对经济观察者的调查进行了培训，然后对新闻文本进行了微调，以供域适应。此外，还应用了一级支持向量机，以滤除与商业情绪无关的文本。此外，我们提出了一种简单的方法，可以暂时分析任何给定因素影响预测的商业情绪。通过一系列关于2013年至2018年发表的Nikkei报纸文章的实验，通过一系列实验来证明所提出方法的有效性和实用性。
This paper describes our work on developing a new business sentiment index using daily newspaper articles. We adopt a recurrent neural network (RNN) with Gated Recurrent Units to predict the business sentiment of a given text. An RNN is initially trained on Economy Watchers Survey and then fine-tuned on news texts for domain adaptation. Also, a one-class support vector machine is applied to filter out texts deemed irrelevant to business sentiment. Moreover, we propose a simple approach to temporally analyzing how much and when any given factor influences the predicted business sentiment. The validity and utility of the proposed approaches are empirically demonstrated through a series of experiments on Nikkei Newspaper articles published from 2013 to 2018.
