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Sentiment analysis using unsupervised learning for local government elections in South Africa
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2024
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| _version_ | 1867613451268915200 |
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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, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/98196 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:21.406Z |
| 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:2263/98196 Sentiment analysis using unsupervised learning for local government elections in South Africa Marivate, Vukosi u22826476@tuks.co.za Olaleye, Kayode Matloga, Mokgadi Penelope UCTD Sentiment analysis OpenAI Fine-tuning Suspicious patterns User classification Local government election Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2023. Understanding public sentiment is vital for political parties in order for them to be able to structure their election campaigns around voter expectations. The study focuses on unsupervised learning to assess the variation of polarity sentiment in tweets during the 2021 South African local government election campaign. The study uses a pre-trained twitter-roberta-base-sentiment-latest model from Hugging Face and unsupervised lexicon based pre-trained approaches, namely: VADER and TextBlob to determine the polarity sentiment in order to gain insight that could be applied towards informing political campaigns and to see if there are any distinct sentiment patterns or shifts during different phases of the 2021 local government elections campaigns. Furthermore, the study applies the use of suspicious patterns and K-Means methods to classify the users as either bots and human using to be able to identify the user behind the keyboard. The study also make use of OpenAI GPT model to label the dataset for fine-tuning and addresses the issue of class imbalance. VADER and TextBlob results show a significant difference from that of the twitter-roberta-base-sentiment-latest models when comparing the statistical distribution based on the sentiment results and the user classification results. Based on the results, there is a significant variation across all sentiment classes and they vary over time. Furthermore, the results revealed TRBSL and TRBSL** outperforms VADER and TextBlob based on the scores for weighted accuracy and F1-scores. It was discovered that most of the tweets were generated by humans, with only few being identified as bot-generated and having a negative sentiments. Computer Science MIT (Big Data Science) Unrestricted Faculty of Engineering, Built Environment and Information Technology 2024-09-13T11:57:27Z 2024-09-13T11:57:27Z 2024-04 2023-11 Mini Dissertation * A2024 http://hdl.handle.net/2263/98196 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 Sentiment analysis OpenAI Fine-tuning Suspicious patterns User classification Local government election Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title | Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title_full | Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title_fullStr | Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title_full_unstemmed | Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title_short | Sentiment analysis using unsupervised learning for local government elections in South Africa |
| title_sort | sentiment analysis using unsupervised learning for local government elections in south africa |
| topic | UCTD Sentiment analysis OpenAI Fine-tuning Suspicious patterns User classification Local government election |
| url | http://hdl.handle.net/2263/98196 |