Unsupervised machine learning to analyse city logistics through Twitter

TL;DR

This paper presents a methodology using unsupervised machine learning and NLP to analyze Twitter content, revealing public perception and key concepts related to City Logistics through sentiment and content analysis of over 110,000 tweets.

Contribution

It introduces a novel approach combining unsupervised learning and NLP for social media analysis of City Logistics, enabling stakeholder and trend identification.

Findings

Created an Interest Map of City Logistics concepts

Performed sentiment analysis categorizing tweets as positive, negative, or neutral

Analyzed over 110,000 tweets to extract public perception insights

Abstract

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.