无监督机器学习通过Twitter分析城市物流
Unsupervised machine learning to analyse city logistics through Twitter
摘要：
城市物流的特点是多个利益相关者，往往有不同的看法，这样一个复杂的系统。从公共政策的角度来看，确定利益攸关方、问题和趋势是一项艰巨的挑战，传统的观察系统只能部分解决这一问题。如今，社交媒体是公众表达的最大渠道之一，经常用于传达与城市物流相关的意见和内容。这项研究的想法是，分析社交媒体内容可以帮助了解城市物流的公众看法。本文提供了一种从Twitter收集内容并实施机器学习技术（无监督学习和自然语言处理）的方法，以执行内容和情感分析。所提出的方法适用于超过110 000推文包含城市物流关键词。结果允许建立概念的兴趣地图和情感分析，以确定城市物流条目是积极的，消极的或中性的。
英文摘要：
City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analysing social media content could help in understanding the public perception of City logistics. This paper offers a methodology for collecting content from Twitter and implementing Machine Learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.
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。
