Unsupervised machine learning to analyze City Logistics through Twitter

Simon Tamayo (1) , François Combes (2) , Arthur Gaudron (1)

1 CAOR - Centre de Robotique
2 AME-SPLOTT - Systèmes Productifs, Logistique, Organisation des Transports et Travail

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 it 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 proposes 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 building an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.

Mots clés: URBAN LOGISTIC; PUBLIC PERCEPTION; MACHINE LEARNING; SENTIMENT ANALYSIS

Domaines: Machine Learning [stat.ML]; Autre

Simon Tamayo, François Combes, Arthur Gaudron. Unsupervised machine learning to analyze City Logistics through Twitter. 11th International Conference on City Logistics, Jun 2019, DUBROVNIK, France. pp 220-228, ⟨10.1016/j.trpro.2020.03.184⟩. ⟨hal-03164665⟩