用GPT-4o分析2024美国总统选举新闻，量化舆论趋势。
Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process
- 结合Google搜索与GPT-4o的RAG框架，提取新闻信息
- 通过贝叶斯回归生成量化评分，捕捉舆论趋势变化
- 可评估选举过程不确定性，适合政治分析与舆情研究
本文采用Google Search API与GPT-4o模型，结合检索增强生成（RAG）方法，对2024年美国总统选举期间不同时间周期、不同新闻来源的内容进行定性与定量分析。利用GPT模型生成的量化评分，通过贝叶斯回归构建趋势线，并分析回归参数分布以评估选举进程中的不确定性。结果表明，该方法能有效生成具有洞察力的新闻分析，为后续选举过程研究提供关键参考。
原文摘要 · Abstract (English)
The paper considers an approach of using Google Search API and GPT-4o model for qualitative and quantitative analyses of news through retrieval-augmented generation (RAG). This approach was applied to analyze news about the 2024 US presidential election process. Different news sources for different time periods have been analyzed. Quantitative scores generated by GPT model have been analyzed using Bayesian regression to derive trend lines. The distributions found for the regression parameters allow for the analysis of uncertainty in the election process. The obtained results demonstrate that using the GPT models for news analysis, one can get informative analytics and provide key insights that can be applied in further analyses of election processes.
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