生田 祐介

This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r = 0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.

政府や中央銀行が発表する景況感指数は，金融当局の政策決定や企業の生産計画などの拠りどころとして重要な役割を担っている．しかし，これらの指数は大規模なアンケートに基づいており，その算出には大変な手間と時間がかかる．そこで本研究では，ニュース記事を再利用することで，低コストで速報性の高い，新しい景況感指数を算出する手法を提案する．具体的に，外れ値検出モデルにより経済・景気に関係が薄いと判断された記事を除外したうえで，それ以外の記事（の文）に対して景気スコアを予測する．そして，ある期間に発行された記事すべての景気スコアを統合して，その期間の景況感指数を定義する．景気スコアの予測には，近年自然言語処理タスクで広く利用されているBERTをファインチューニングして用いる．さらに，算出された景況感指数を基に，任意の概念（語句）がどのように景況感に影響を与えたのかを時間軸に沿って分析する手法を提案する．