Analyzing Business Conditions by Quantitative Text Analysis–Time Series Analysis Using Appearance Rate and Principal Component

We present a procedure for analyzing the current business conditions and forecasting GDP growth rate by quantitative text analysis. We use text data of Economy Watcher Survey conducted by Cabinet Office. We extract words from 190 thousands sentence, and construct time series data by counting appearance rate every month. The analyses consist of four parts: (1) visualizing appearance rate by drawing graphs, (2) correlation analysis, (3) principal component analysis, and (4) forecasting GDP growth rate. First, we draw graphs of the appearance rate of words which are influenced by business conditions. We find that the graphs show the effect of policy on business conditions clearly. Second, we construct word lists which correlate business conditions by computing correlation coefficients. And we also construct lists which reversely correlate business conditions. Third, we extract principal component from 150 frequent words. We find that the 1st principal component move together with business conditions. The last, we forecast quarterly real GDP growth rate by text data. We find that forecast accuracy improved by adding the text data. It shows that text data have useful information about GDP forecasting.