  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.
