Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Murcos, F (Autor) Outra; Carneiro, E (Autor) Outra; Ribeiro, J (Autor) Outra; Rosaldo J. F. Rossetti (Autor) FEUP

Revista

A Revista está pendente de validação pelos Serviços Administrativos.

Título: IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

Vol. 4

Páginas: 663-681

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00Y-YSS

DOI: 10.1109/ojits.2023.3308210

Abstract (EN): This work presents a deep learning framework for analyzing urban mobility by extracting knowledge from messages collected from Twitter. The framework, which is designed to handle large-scale data and adapt automatically to new contexts, comprises three main modules: data collection and system configuration, data analytics, and aggregation and visualization. The text data is pre-processed using NLP techniques to remove informal words, slang, and misspellings. A pre-trained, unsupervised word embedding model, BERT, is used to classify travel-related tweets using a unigram approach with three dictionaries of travel-related target words: small, medium, and big. Public opinion is evaluated using VADER to classify travel-related tweets according to their sentiments. The mobility of three major cities was assessed: London, Melbourne, and New York. The framework demonstrates consistently high average performance, with a Precision of 0.80 for text classification and 0.77 for sentiment analysis. The framework can aggregate sparse information from social media and provide updated information in near real-time with high spatial resolution, enabling easy identification of traffic-related events. The framework is helpful for transportation decision-makers in operational control, tactical-strategic planning, and policy evaluation. For example, it can be used to improve the management of resources during traffic congestion or emergencies.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19