  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.
