用大模型细分日本调查中的价格情绪,区分消费者与企业视角。
Refined and Segmented Price Sentiment Indices from Survey Comments
- 用大模型分析调查评论,区分消费者/企业、商品/服务视角。
- 新指数与已有指数相关性更高,消费者类指数因按行业筛选而更优。
- 多模型融合提升分类效果,适合宏观经济研究者参考。
本文旨在提升价格情绪指数的精度,从消费者和企业双重视角理解价格趋势。基于日本内阁府开展的经济观察者调查(Economy Watchers Survey),我们提取与价格相关的评论,并利用大语言模型(LLM)对价格趋势进行分类。通过评论所属领域及受访者行业信息,区分评论反映的是消费者还是企业视角,以及涉及商品或服务。基于这些分类结果,构建了兼顾消费者与企业、商品与服务的细化价格情绪指数。使用大模型显著提升了价格方向分类的准确性,多模型输出集成进一步增强分类性能。更精准的评论分类使新指数与既有指数的相关性优于以往研究。特别地,由于样本量更大,按受访者行业筛选后的消费者价格指数相关性进一步提升。
原文摘要 · Abstract (English)
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.
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