S-APIR: News-based Business Sentiment Index

• URL: http://arxiv.org/abs/2003.02973v1
• Date: Fri, 6 Mar 2020 00:18:50 GMT
• Title: S-APIR: News-based Business Sentiment Index
• Authors: Kazuhiro Seki and Yusuke Ikuta
• Abstract summary: 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. A one-class support vector machine is applied to filter out texts deemed irrelevant to business sentiment.
• Score: 0.9137554315375919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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