S-APIR：基于新闻的商业景气指数
S-APIR: News-based Business Sentiment Index
摘要：
本文介绍了我们利用每日报纸文章开发新的商业景气指数的工作。我们采用一个带有门控递归单元的递归神经网络（RNN）来预测给定文本的商业情绪。RNN最初在经济观察者调查上进行训练，然后在新闻文本上进行微调以适应领域自适应。此外，一类支持向量机被应用于过滤被认为与商业情绪无关的文本。此外，我们提出了一个简单的方法来分析有多少时间和任何给定的因素影响预测的商业情绪。通过对2013年至2018年出版的日经新闻文章进行的一系列实验，实证证明了所提出方法的有效性和实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
