用大模型分析日本工资民意，预测薪资趋势
Wage Sentiment Indices Derived from Survey Comments via Large Language Models
- 用大模型解析经济观察者调查中的评论，构建工资情绪指数
- 大模型方法显著优于传统基线和预训练模型
- 适合关注宏观经济预测与政策制定的研究者
生成式人工智能的兴起为经济文本分析带来了新机遇。本研究提出基于大语言模型（LLMs）的工资情绪指数（WSI），用于预测日本的工资动态。分析基于日本内阁府每月开展的经济观察者调查（EWS），该调查捕捉对商业状况高度敏感行业的劳动者实时经济评估。WSI扩展了以往研究中价格情绪指数（PSI）的框架，专门适配工资相关情绪分析。为确保可扩展性和适应性，还设计了一套数据架构，支持后续整合报纸、社交媒体等其他数据源。实验结果表明，基于LLM的WSI模型显著优于基线方法和预训练模型。研究凸显了大模型驱动的情绪指数在提升经济政策制定时效性与有效性方面的潜力。
原文摘要 · Abstract (English)
The emergence of generative Artificial Intelligence (AI) has created new opportunities for economic text analysis. This study proposes a Wage Sentiment Index (WSI) constructed with Large Language Models (LLMs) to forecast wage dynamics in Japan. The analysis is based on the Economy Watchers Survey (EWS), a monthly survey conducted by the Cabinet Office of Japan that captures real-time economic assessments from workers in industries highly sensitive to business conditions. The WSI extends the framework of the Price Sentiment Index (PSI) used in prior studies, adapting it specifically to wage related sentiment. To ensure scalability and adaptability, a data architecture is also developed that enables integration of additional sources such as newspapers and social media. Experimental results demonstrate that WSI models based on LLMs significantly outperform both baseline approaches and pretrained models. These findings highlight the potential of LLM-driven sentiment indices to enhance the timeliness and effectiveness of economic policy design by governments and central banks.
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