Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019

TL;DR

This paper analyzes Twitter data from 2007 to 2019 to understand public opinions and critical stakeholder groups regarding NYC congestion pricing proposals, revealing evolving concerns and influential actors.

Contribution

It introduces a hybrid NLP approach combining LDA and LSTM to study temporal opinion shifts and identify key interest groups in a major urban policy debate.

Findings

Multiple interest groups influenced the proposal process

Public concern shifted towards sustainability and fairness

Approval depended on political consensus and awareness of tolling benefits

Abstract

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.