Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process
Bohdan M. Pavlyshenko
TL;DR
This paper presents a novel approach using GPT-4 and Google Search API for analyzing news related to the 2024 US presidential election, combining qualitative insights with quantitative trend analysis.
Contribution
It introduces a retrieval-augmented generation method for news analysis and applies Bayesian regression to interpret GPT-generated scores in election trend forecasting.
Findings
GPT models provide informative news analytics.
Bayesian regression reveals election trend uncertainties.
Quantitative scores help track election process dynamics.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational and Text Analysis Methods
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Dense Connections · Layer Normalization · Residual Connection · Cosine Annealing · Weight Decay · Linear Layer · Softmax · Multi-Head Attention · Dropout