Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study

TL;DR

This study analyzes public sentiment toward Knoxville's traffic policies using social media data from Twitter and Reddit, revealing predominantly negative sentiment and platform-specific differences, demonstrating social media's utility for transportation policy monitoring.

Contribution

It introduces a social media-based methodology for real-time public sentiment analysis of traffic policies, combining sentiment and topic modeling across platforms.

Findings

Twitter shows more negative sentiment than Reddit.

Construction topics are most negatively perceived.

Sentiment varies geographically and temporally.

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.