We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sample reflects the perspective of consumers or businesses, and whether the comments pertain to goods or services by utilizing information on the fields of comments and the industries of respondents included in the Economy Watchers Survey. From these classified price-related comments, we construct price sentiment indices not only for a general purpose but also for more specific objectives by combining perspectives on consumers and prices, as well as goods and services. It becomes possible to achieve a more accurate classification of price directions by employing a LLM for classification. Furthermore, integrating the outputs of multiple LLMs suggests the potential for the better performance of the classification. The use of more accurately classified comments allows for the construction of an index with a higher correlation to existing indices than previous studies. We demonstrate that the correlation of the price index for consumers, which has a larger sample size, is further enhanced by selecting comments for aggregation based on the industry of the survey respondents.

翻译：本研究旨在优化价格情绪指数，并分别从消费者与企业的视角更精确地理解价格变动趋势。我们提取日本内阁府经济观察者调查中与价格相关的评论，并运用大语言模型对价格趋势进行分类。通过利用调查数据中评论领域及受访者行业信息，我们对调查样本进行双重分类：区分评论视角属于消费者或企业，以及评论对象属于商品或服务。基于分类后的价格相关评论，我们通过组合消费者/企业视角与商品/服务维度，构建了适用于通用场景及特定目标的精细化价格情绪指数。采用大语言模型进行分类能显著提升价格趋势方向的判别精度，而融合多个大语言模型输出结果的方法展现出进一步提升分类性能的潜力。相较于既有研究，使用经精确分类的评论所构建的指数与现有指标具有更高的相关性。我们进一步证明，对于样本量更大的消费者价格指数，通过依据受访者行业筛选评论进行聚合，能有效提升其与基准指标的相关性。