论文标题
预测经济新闻
Forecasting with Economic News
论文作者
论文摘要
本文的目的是评估有关经济状况的新闻文章中提取的情感的信息内容。我们提出了具有两个主要特征的基于方面的精细情感分析：1）我们仅考虑文章中的文本，其语义上取决于感兴趣的术语（基于方面）和，2）为每个单词分配一个基于我们为经济学和融资中应用的词典（fine fine）（细元）开发的词典。我们的数据集包括六本大型美国报纸，总共有660万篇文章和42亿个单词。我们的发现表明，经济情绪的几种衡量标准跟踪了商业周期的波动，它们是四个主要宏观经济变量的相关预测因素。我们发现，当考虑情绪以及宏观经济因素时，预测有了显着改善。此外，我们还发现，情绪要解释了几个宏观经济变量的概率分布的尾巴。
The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, 2) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our data set includes six large US newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.
