使用LDA和LSTM模型研究2007年至2019年纽约市对拥堵定价的公众意见和批评群体
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019
摘要：
本研究探讨了人们如何看待和回应纽约市拥挤收费的建议演变的时间。为了了解这些反应，Twitter数据被收集和分析。通过统计分析活跃用户和最常提及的帐户来检测循环过程中的关键组，并通过文本挖掘和混合自然语言处理技术（包括LDA主题建模和LSTM情感分类）来识别多年来人们的态度和关注的趋势。研究结果显示，多个利益团体参与，并发挥了关键作用，在建议过程中，特别是市长和州长，MTA，和外区代表。公众将关注的焦点从规划细节转移到更广泛的城市的可持续性和公平性上。此外，该计划的批准依赖于几个因素，在政治进程中达成的联合协议，现实世界中的强大动机，基于平衡多方利益的计划，以及团体对收费的好处和必要性的认识。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
