Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019
This work provides insights into public sentiment and political dynamics for policymakers and urban planners in NYC, but it is incremental as it applies existing NLP methods to a specific domain dataset.
This study analyzed Twitter data from 2007 to 2019 to track evolving public opinions and identify key groups influencing congestion pricing proposals in New York City, finding that interest groups like the Mayor and MTA played crucial roles and public concerns shifted from plan details to broader sustainability and fairness issues.
This study explores how people view and respond to the proposals of NYC congestion pricing evolve in time. To understand these responses, Twitter data is collected and analyzed. Critical groups in the recurrent process are detected by statistically analyzing the active users and the most mentioned accounts, and the trends of people's attitudes and concerns over the years are identified with text mining and hybrid Nature Language Processing techniques, including LDA topic modeling and LSTM sentiment classification. The result shows that multiple interest groups were involved and played crucial roles during the proposal, especially Mayor and Governor, MTA, and outer-borough representatives. The public shifted the concern of focus from the plan details to a wider city's sustainability and fairness. Furthermore, the plan's approval relies on several elements, the joint agreement reached in the political process, strong motivation in the real-world, the scheme based on balancing multiple interests, and groups' awareness of tolling's benefits and necessity.