We reveal the different interpretations of the future in their judgments of future economic conditions by applying weakly supervised learning and text mining. In the Economy Watcher Survey, which is a market survey published by the Japanese government, there are assessments of current and future economic conditions by people from various fields. Although this survey provides insights regarding an economic policy for policymakers in Japan, there is no clear definition of the future, in future economic conditions  . Hence,  in the survey , respondents make their assessments based on their interpretations of the future. In our research, we separate the assessments of future economic conditions into  near and distant future economic conditions using learning from positive and unlabeled data (PU learning) , which is weakly supervised learning. The dataset is composed of several periods, and we develop a PU learning algorithm for efficient training, using the dataset with the time series. Through empirical analysis , we interpret the classification results from the viewpoint of economics .
The Economy Watcher Survey, which is a market survey published by the Japanese government, contains assessments of current and future economic conditions by people from various fields. Although this survey provides insights regarding economic policy for policymakers , a clear definition of the word "future" in future economic conditions is not provided . Hence, the assessments respondents provide in the survey are simply based on their interpretations of the meaning of "future." This motivated us to reveal the different interpretations of the future in their judgments of future economic conditions by applying weakly supervised learning and text mining. In our research, we separate the assessments of future economic conditions into economic conditions of the near and distant future using learning from positive and unlabeled data (PU learning) . Because the dataset includes data from several periods, we devised new architecture to enable neural networks to conduct PU learning based on the idea of multi-task learning to efficiently learn a classifier. Our empirical analysis confirmed that the proposed method could separate the future economic conditions, and we interpreted the classification results to obtain intuitions for policymaking .
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