Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study
Shampa Saha, Shovan Roy
TL;DR
Public sentiment toward traffic management policies is variable and often negative, especially around construction and parking. The authors combine Twitter and Reddit data from 2022–2023 with VADER sentiment scoring and LDA topic modeling to map city-wide opinions in Knoxville. The study finds predominantly negative sentiment, with stronger negativity on Twitter, and six topics with construction-related discussions driving the most negative reactions, while general traffic topics are more positive; spatiotemporal patterns reveal location- and time-based hotspots. The work demonstrates that social media can serve as a real-time, scalable tool for transportation planning and policy evaluation, informing proactive communication, parking strategies, and targeted program enhancements.
Abstract
This study presents a comprehensive analysis of public sentiment toward traffic management policies in Knoxville, Tennessee, utilizing social media data from Twitter and Reddit platforms. We collected and analyzed 7906 posts spanning January 2022 to December 2023, employing Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling. Our findings reveal predominantly negative sentiment, with significant variations across platforms and topics. Twitter exhibited more negative sentiment compared to Reddit. Topic modeling identified six distinct themes, with construction-related topics showing the most negative sentiment while general traffic discussions were more positive. Spatiotemporal analysis revealed geographic and temporal patterns in sentiment expression. The research demonstrates social media's potential as a real-time public sentiment monitoring tool for transportation planning and policy evaluation.