Analysing public transport user sentiment

Abstract

In many Sub-Saharan countries, the advancement of public transport is frequently overshadowed by more prioritised sectors, highlighting the need for innovative approaches to enhance both the Quality of Service (QoS) and the overall user experience. This research aimed at mining the opinions of commuters to shed light on the prevailing sentiments regarding public transport systems. Concentrating on the experiential journey of users, the study adopted a qualitative research design, utilising real-time data gathered from Twitter to analyse sentiments across three major public transport modes: rail, mini-bus taxis, and buses. By employing Multilingual Opinion mining techniques, the research addressed the challenges posed by linguistic diversity and potential code-switching in the dataset, showcasing the practical application of Natural Language Processing (NLP) in extracting insights from under-resourced language data. The primary contribution of this study lies in its methodological approach, offering a framework for conducting sentiment analysis on multilingual and low-resource languages within the context of public transport. The findings hold potential implications beyond the academic realm, providing transport authorities and policymakers with a methodological basis to harness technology in gaining deeper insights into public sentiment. By prioritising the analysis of user experiences and sentiments, this research provides a pathway for the development of more responsive, usercentered public transport systems in Sub-Saharan countries, thereby contributing to the broader objective of improving urban mobility and sustainability.