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.

翻译：本文探讨了一种利用Google搜索API与GPT-4o模型，通过检索增强生成（RAG）技术对新闻进行定性与定量分析的方法。该方法被应用于分析2024年美国总统选举过程的相关新闻。研究对不同时间段、不同新闻来源的报道进行了分析。通过贝叶斯回归对GPT模型生成的定量评分数据进行趋势线拟合，所得回归参数的分布可用于分析选举过程中的不确定性。研究结果表明，利用GPT模型进行新闻分析能够获得信息丰富的分析结果，并提供可用于后续选举过程分析的关键见解。