The Effectiveness of Sentiment Analysis for Detecting Fine-grained Service Quality. GeoComputation 2019
conference contributionposted on 01.12.2019 by Mohammad Masoud Rahimi, ELHAM NAGHI ZADEH KAKHKI, STEPHAN WINTER, MARK STEVENSON
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
Improving public transport relies on the development of appropriate tools for measuring and monitoring service quality. While there are several studies exploring perceived service quality, these works are mainly based on surveys, which are limited to a pre-defined period and do not enable to constantly observe and analyze the respond of passengers to different events. As a result, they may miss events affecting service quality in a transport hub. In contrast, social media feeds provide an opportunity to constantly look for events that may affect the service quality. However, in a confined geographic area like a transport hub, the sparsity of data considerably reduce the effectiveness of straight-forward event detection methods. In contrast
to earlier works and in order to face these challenges, in this paper, we explore the effectiveness of sentiment analysis to detect events impacting perceived service quality in a confined location. Southern Cross Station, a major transport hub in Melbourne, Australia, is selected as a case
study. Moreover, a statistical approach to combine and integrate sentiment-based and frequency-based
event detection is proposed. Main findings conrm the effectiveness of sentiment analysis for detecting events affecting service quality in a transport hub. Additionally, the results prove that the proposed method can improve the sensitivity of event detection for events affecting