Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Analysing public transport user sentiment
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2024.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2024
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613650092556288 |
|---|---|
| access_status_str | Open Access |
| author2 | Marivate, Vukosi |
| author_browse | Marivate, Vukosi |
| author_facet | Marivate, Vukosi |
| collection | Thesis |
| dc_rights_str_mv | © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2024. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/98180 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:30.899Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/98180 Analysing public transport user sentiment Marivate, Vukosi Abdulmumin, Idris Myoya, Rozina L. UCTD Sub-Saharan Africa (SSA) Public transport Quality of Service (QoS) Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2024. 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. Computer Science MIT (Big Data Science) Unrestricted Faculty of Engineering, Built Environment and Information Technology 2024-09-13T09:42:26Z 2024-09-13T09:42:26Z 2024-04 2024 Mini Dissertation * A2024 http://hdl.handle.net/2263/98180 en © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sub-Saharan Africa (SSA) Public transport Quality of Service (QoS) Analysing public transport user sentiment |
| title | Analysing public transport user sentiment |
| title_full | Analysing public transport user sentiment |
| title_fullStr | Analysing public transport user sentiment |
| title_full_unstemmed | Analysing public transport user sentiment |
| title_short | Analysing public transport user sentiment |
| title_sort | analysing public transport user sentiment |
| topic | UCTD Sub-Saharan Africa (SSA) Public transport Quality of Service (QoS) |
| url | http://hdl.handle.net/2263/98180 |