Project ISF: Harnessing City Conversations to Infer Public Opinion Towards Renewable Energy Deployment Program DUIRI - Discovery Undergraduate Interdisciplinary Research Internship Term Fall 2024 Status Accepted Research Area AI, Sustainability, Data Science, Public Policy, Renewable Energy Description The goal of this research is to examine the temporal trend and spatial pattern of mass public opinion towards renewable energy. We will use topical modeling methods to analyze the LocalView dataset (a large dataset that compiles meeting minutes from over 1000 local governments in the U.S.) to identify topics related to renewable energy discussed at city meetings. Sentiment analysis will be conducted to investigate public perceptions of large-scale renewable energy siting and energy (in)justice. Supervisor Shan Zhou Mentor Gaurav Nanda Type of work required The project will involve application of Natural Language Processing and Machine Learning approaches to analyze public governmental and municipal meetings data on topics of sustainability, green energy, and other related issues. The student will work on extracting textual data from databases and website, analyze the data using unsupervised machine learning and natural language processing approaches including sentiment analysis, topic modeling, and using large language models.
Websites Qualifications The student will be expected to be proficient in programming in Python, completed coursework on machine learning and natural language processing, and have some experience in working with real-world data which is typically noisy in nature. Credit Hours 0 Weekly Hours 10 (estimated)