AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity

TL;DR

This paper explores using GPT-4.1 with retrieval-augmented generation to analyze NATO-related news and opinions across various online sources, providing qualitative summaries and quantitative trend analysis of NATO sentiment.

Contribution

It introduces a novel AI-based approach combining GPT-4.1 and Bayesian regression for multi-level qualitative and quantitative NATO news analytics, including opinion trend modeling.

Findings

NATO opinion scores show a downward trend.

GPT models can generate informative qualitative summaries.

Bayesian regression captures uncertainty in opinion trends.

Abstract

The paper considers the use of GPT models with retrieval-augmented generation (RAG) for qualitative and quantitative analytics on NATO sentiments, NATO unity and NATO Article 5 trust opinion scores in different web sources: news sites found via Google Search API, Youtube videos with comments, and Reddit discussions. A RAG approach using GPT-4.1 model was applied to analyse news where NATO related topics were discussed. Two levels of RAG analytics were used: on the first level, the GPT model generates qualitative news summaries and quantitative opinion scores using zero-shot prompts; on the second level, the GPT model generates the summary of news summaries. Quantitative news opinion scores generated by the GPT model were analysed using Bayesian regression to get trend lines. The distributions found for the regression parameters make it possible to analyse an uncertainty in specified news opinion score trends. Obtained results show a downward trend for analysed scores of opinion related to NATO unity. This approach does not aim to conduct real political analysis; rather, it consider AI based approaches which can be used for further analytics as a part of a complex analytical approach. The obtained results demonstrate that the use of GPT models for news analysis can give informative qualitative and quantitative analytics, providing important insights. The dynamic model based on neural ordinary differential equations was considered for modelling public opinions. This approach makes it possible to analyse different scenarios for evolving public opinions.