Identifying Different Definitions of Future in the Assessment of Future Economic Conditions: Application of PU Learning and Text Mining

TL;DR

This paper uses PU learning and text mining to analyze how respondents interpret 'future' in economic assessments, distinguishing near and distant future conditions to aid policymakers.

Contribution

It introduces a novel neural network architecture for PU learning applied to economic survey data, revealing different interpretations of 'future' in economic conditions.

Findings

Successfully separated near and distant future economic assessments

Provided insights into respondents' varied interpretations of 'future'

Demonstrated the effectiveness of multi-task neural networks for PU learning

Abstract

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.