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.
