Citation

Qi B, Costin A, Jia M. Travel Behav. Soc. 2020; 21: 10-23.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.tbs.2020.05.005

PMID

unavailable

Abstract

Public opinion is a valuable source for evaluating the performance of transportation services, which can guide management and policy adjustment. In contrast to traditional surveying methods, which are often inefficient and high-cost, social media has become a popular channel to collect public opinion due to its large data volume and accessibility characteristics. However, existing research lack efficient methods to extract and interpret Twitter data for transportation services evaluation. Therefore, this paper presents a comprehensive framework that enables high-efficient extraction and analysis of public opinions on transportation services from Twitter. The transportation system of Miami-Dade County is chosen as the case study to describe the framework development and validation process. First, Twitter data in a defined area over a certain period are collected and preprocessed to clean erroneous and redundant information. Then, the Twitter data that relate to personal opinions on transportation services (POTS) are filtered hieratically using text classification models trained by manually labeled dataset. Next, timeline analysis using Post Intensity (PI) and Average Sentiment Value (ASV) is implemented on the selected Twitter data to explore the public perceptions towards the transportation-related events. Topic modeling and tokenization techniques are also adopted to extract relevant semantic content needed for data analysis. Significantly, the developed framework can improve the efficiency of Twitter data extraction and analysis. Many manual steps are involved in the development process of the framework while can be avoided when generalized to other applications. The framework can be used as a tool for stakeholders to enable a holistic understanding of public opinions on transportation services and increase the degree of public participation in transportation management.

Language: en

Keywords

Crowdsourcing; Machine learning; Sentiment analysis; Text classification; Transportation services