Twitter to Transport: Geo-Spatial Sentiment Analysis of Traffic Tweets to Discover People’s Feelings for Urban Transportation Issues

Abstract

Transportation is an essential part of human life, therefore, it is important to understand the opinion of the general public in different aspects of transportation. Furthermore, the citizen’s opinion is the actual customer-based performance metric which can be used to assign priorities to the urban traffic issues. Further, Social media is the cost effective source of large amount of dataset, which provides the possibility of opinion mining in this context. This paper proposes an opinion mining approach based on traffic-related tweets to find the citizen’s sentiment for urban transportation issues. In order to show the prevalence of transportation on social media, the location-based traffic related tweets, written by individuals expressing their sentiments about different transport services have been mined, preprocessed, and then a dictionary-based approach is used for the calculation of sentiment and classification of sentiment polarity to evaluate the satisfaction of transportation users.