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Ukhetho : a text mining study of the South African general elections
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2019.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613453226606592 |
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| 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, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/82552 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:23.211Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/82552 Ukhetho : a text mining study of the South African general elections Marivate, Vukosi avashlin@gmail.com Moodley, Avashlin UCTD Election analysis Natural language processing (NLP) Text mining Latent dirichlet allocation Non-negative matrix factorization Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2019. The elections in South Africa are contested by multiple political parties appealing to a diverse population that comes from a variety of socioeconomic backgrounds. As a result, a rich source of discourse is created to inform voters about election-related content. Two common sources of information to help voters with their decision are news articles and tweets, this study aims to understand the discourse in these two sources using natural language processing. Topic modelling techniques, Latent Dirichlet Allocation and Non- negative Matrix Factorization, are applied to digest the breadth of information collected about the elections into topics. The topics produced are subjected to further analysis that uncovers similarities between topics, links topics to dates and events and provides a summary of the discourse that existed prior to the South African general elections. The primary focus is on the 2019 elections, however election-related articles from 2014 and 2019 were also compared to understand how the discourse has changed. bs2026 Computer Science MIT (Big Data Science) Unrestricted SDG-04: Quality education SDG-09: Industry, innovation and infrastructure SDG-16: Peace, justice and strong institutions 2021-11-03T11:32:10Z 2021-11-03T11:32:10Z 2020 2019 Mini Dissertation * http://hdl.handle.net/2263/82552 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 Election analysis Natural language processing (NLP) Text mining Latent dirichlet allocation Non-negative matrix factorization Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 Ukhetho : a text mining study of the South African general elections |
| title | Ukhetho : a text mining study of the South African general elections |
| title_full | Ukhetho : a text mining study of the South African general elections |
| title_fullStr | Ukhetho : a text mining study of the South African general elections |
| title_full_unstemmed | Ukhetho : a text mining study of the South African general elections |
| title_short | Ukhetho : a text mining study of the South African general elections |
| title_sort | ukhetho a text mining study of the south african general elections |
| topic | UCTD Election analysis Natural language processing (NLP) Text mining Latent dirichlet allocation Non-negative matrix factorization Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-16 |
| url | http://hdl.handle.net/2263/82552 |