Unsupervised machine learning to analyse city logistics through Twitter
This work addresses the challenge for public policymakers in identifying stakeholders and trends in city logistics, though it is incremental by applying existing methods to social media data.
The paper tackled the problem of understanding public perception of city logistics by analyzing over 110,000 tweets using unsupervised machine learning and NLP, resulting in an interest map of concepts and sentiment analysis categorizing entries as positive, negative, or neutral.
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.