  The Economy Watcher Survey, which is a market survey published by the
Japanese government, contains \emph{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.
