Research Summary: Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis

A new research study introduces an innovative, fully automated pipeline utilizing Large Language Model (LLM) agents for multi-task public opinion analysis. This pipeline offers an end-to-end analytical workflow that does not require domain-specific training data, manual annotation, or local deployment. It is designed to be low-cost, user-friendly, and accessible, enabling timely and integrated public opinion analysis through a single natural language query.

Why it matters

This development in AI-driven public opinion analysis has the potential to transform how organizations monitor and understand public sentiment. Its automated, accessible nature could enable faster, more comprehensive insights, influencing strategic decision-making across various domains and reducing the reliance on traditional, resource-intensive methods.

Key insights

• The research proposes the first LLM agents-based agentic pipeline for multi-task public opinion analysis.
• The pipeline enables a fully automated, end-to-end analytical workflow, eliminating the need for domain-specific training data or manual annotation.
• It integrates advanced LLM capabilities into a low-cost, user-friendly framework, suitable for resource-constrained environments.
• The system allows for timely and integrated public opinion analysis via a single natural language query, making it accessible to non-expert users.
• Initial validation involved a real-world case study analyzing 1,572 social media posts related to a hypothetical future tariff dispute.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2505.11401