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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Murcos, F (Author) Other; Carneiro, E (Author) Other; Ribeiro, J (Author) Other; Rosaldo J. F. Rossetti (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

Vol. 4

Pages: 663-681

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 7 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19