Estimating Business Sentiment from News Texts

Business sentiment indices play a crucial role in government's policy decisions, production planning in industries, and individual/institutional investment decisions. However, such indices require large-scale surveys, which are costly and laborious to conduct. As an alternative, this paper reports our ongoing work on estimating business sentiment based on abundant, ordinary newspaper articles. Our proposed framework is composed of a recurrent neural network for regression, a one-class support vector machine to filter out topically irrelevant articles, and fine-tuning for domain adaptation. The effectiveness of the framework is empirically demonstrated by experiments on Nikkei newspaper.