Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911303459667968 |
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| author | Saha, Shampa Roy, Shovan |
| author_facet | Saha, Shampa Roy, Shovan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study Saha, Shampa Roy, Shovan Social and Information Networks Artificial Intelligence 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. |
| title | Public Sentiment Analysis of Traffic Management Policies in Knoxville: A Social Media Driven Study |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2512.03103 |