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South African isiZulu and siSwati news corpus creation, annotation and categorisation
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613688815419392 |
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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, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/92767 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:07.894Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| 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:2263/92767 South African isiZulu and siSwati news corpus creation, annotation and categorisation Marivate, Vukosi u18114564@tuks.co.za Adendorff, M. Madodonga, Andani UCTD South African Local Languages Low Resources Languages Data Augmentation Topic Classification Logistic regression 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-10 Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2022. South Africa has eleven official languages and amongst the eleven languages only 9 languages are local low-resourced languages. As a result, it is essential to build the resources for these languages so that they can benefit from advances in the field of natural language processing. In this project, the focus was to create annotated datasets for the isiZulu and siSwati local languages based on news topic classification tasks and present the findings from these baseline classification models. Due to the shortage of data for these local South African languages, the datasets that were created were augmented and oversampled to increase data size and overcome class classification imbalance. In total, four different classification models were used namely Logistic regression, Naive bayes, XGBoost and LSTM. These models were trained on three different word embeddings namely Count vectorizer, TFIDF vectorizer and word2vec. The results of this study showed that XGBoost, Logistic regression and LSTM, trained from word2vec performed better than the other combinations. bs2026 Computer Science MIT (Big Data Science) Unrestricted SDG-04: Quality education SDG-09: Industry, innovation and infrastructure SDG-10: Reduced inequalities 2023-10-09T08:01:33Z 2023-10-09T08:01:33Z 2023-04 2022 Mini Dissertation * A2023 http://hdl.handle.net/2263/92767 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 South African Local Languages Low Resources Languages Data Augmentation Topic Classification Logistic regression 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-10 South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title | South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title_full | South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title_fullStr | South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title_full_unstemmed | South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title_short | South African isiZulu and siSwati news corpus creation, annotation and categorisation |
| title_sort | south african isizulu and siswati news corpus creation annotation and categorisation |
| topic | UCTD South African Local Languages Low Resources Languages Data Augmentation Topic Classification Logistic regression 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-10 |
| url | http://hdl.handle.net/2263/92767 |