Enabling real-time Sentiment Analysis for Clean Energy Transition with Large Language Models – UROP Spring Symposium 2025

Enabling real-time Sentiment Analysis for Clean Energy Transition with Large Language Models

Huawen Shen

Research Mentor(s): Majdi Radaideh
Mentor Department: Nuclear Engineering and Radiological Sciences
Authors:
Session: Session 5 (2:00pm – 2:50pm)
Presentation Type: Poster 36

Abstract

This project aims to develop a real-time sentiment analysis system for assessing public opinion on clean energy across multiple social media platforms. Building on our previous research, which successfully utilized large language models (LLMs) to analyze sentiment about nuclear power on X/Twitter, this initiative seeks to expand the technology to cover a broader range of clean energy sources and additional platforms such as Facebook, LinkedIn, and Reddit.

lsa logoum logo