S-APIR: News-based Business Sentiment Index
This work provides a tool for economists and analysts to monitor business sentiment from news data, but it is incremental as it builds on existing NLP and sentiment analysis techniques.
The paper tackled the problem of creating a business sentiment index from daily newspaper articles by developing a method using an RNN with GRUs and a one-class SVM for filtering, and demonstrated its validity through experiments on Nikkei Newspaper articles from 2013 to 2018.
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.