Exploring COVID-19 public perceptions in South Africa through sentiment analysis and topic modelling of Twitter posts
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Date
Authors
Kekere, Temitope
Marivate, Vukosi
Hattingh, Maria J. (Marie)
Journal Title
Journal ISSN
Volume Title
Publisher
Learning Information Networking and Knowledge (LINK) Centre, Graduate School of Public and Development
Abstract
The narratives shared on social media during a health crisis such as COVID-19
reflect public perceptions of the crisis. This article provides findings from a study of
the perceptions of South African citizens regarding the government’s response to
the COVID-19 pandemic from March to May 2020. The study analysed Twitter
data from posts by government officials and the public in South Africa to measure
the public’s confidence in how the government was handling the pandemic. A third
of the tweets dataset was labelled using valence aware dictionary and sentiment
reasoner (VADER) lexicons, forming the training set for four classical machinelearning
algorithms—logistic regression (LR), support vector machines (SVM),
random forest (RF), and extreme gradient boosting (XGBoost)—that were employed
for sentiment analysis. The effectiveness of these classifiers varied, with error rates of
17% for XGBoost, 14% for RF, and 7% for both SVM and LR. The best-performing
algorithm (SVM) was subsequently used to label the remaining two-thirds of the
tweet dataset. In addition, the study used, and evaluated the effectiveness of, two
topic-modelling algorithms—latent dirichlet allocation (LDA) and non-negative
matrix factorisation (NMF)—for classification of the most frequently occurring
narratives in the Twitter data. The better-performing of these two algorithms, NMF,
identified a prevalence of positive narratives in South African public sentiment
towards the government’s response to COVID-19.
Description
Keywords
Sentiment analysis, Sentiment classification, Topic modelling, Social media, Twitter, Natural language processing (NLP), Government response, Public perceptions, COVID-19 pandemic, Coronavirus disease 2019 (COVID-19), South Africa (SA), SDG-09: Industry, innovation and infrastructure, Valence aware dictionary and sentiment reasoner (VADER), Logistic regression (LR), Extreme gradient boosting (XGBoost), Support vector machines (SVM), Random forest (RF), Non-negative matrix factorisation (NMF), Latent dirichlet allocation (LDA)
Sustainable Development Goals
SDG-09: Industry, innovation and infrastructure
Citation
Kekere, T., Marivate, V., & Hattingh, M. (2023). Exploring COVID-19 public
perceptions in South Africa through sentiment analysis and topic modelling of
Twitter posts. The African Journal of Information and Communication (AJIC), 31,
1-27. https://DOI.org/10.23962/ajic.i31.14834.