This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.
翻译:本文介绍了一种根据文字数据衡量商业情绪的方法; 商业情绪是通过传统调查来衡量的,传统调查费用昂贵,耗费时间; 为了解决问题,我们利用日报文章,并采用一种以自我注意为基础的模式来界定称为S-APIR的商业情绪指数,在这种模式中,对外观检测模型进行调查,以适当处理各种新闻文章的种类; 此外,我们提出一种简单的方法,在时间上分析任何特定事件对预测商业情绪指数的贡献程度; 为了证明拟议做法的有效性,对报纸文章的价值进行了12年的广泛分析; 分析表明,S-APIR指数与既定的基于调查的指数(在相关系数r=0.937之前)有着密切和积极的关联,而且外观检测特别对一般报纸是有效的; 此外,S-APIR与各种经济指数进行了比较,揭示S-APIR的特性,表明它反映了宏观经济趋势以及经济行为主体的经济前景和情绪; 此外,为了说明S-APIR指数如何有利于经济学家和决策者,对企业对时间的影响进行了若干事件的分析。