Browsing by Author "Bateesa, Tobius Saul"

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    A Comparison of Topic Modeling and Classification Machine Learning Algorithms on Luganda Data
    (AfricaNLP workshop, 2022) Bateesa, Tobius Saul; Babirye, Claire; Nakatumba-Nabende, Joyce
    Extracting functional themes and topics from a large text corpus manually is usually infeasible. There is a need to build text mining techniques such as topic modeling, which provide a mechanism to infer topics from a corpus of text automatically. This paper discusses topic modeling and topic classification models on Luganda text data. For topic modeling, we considered a Non-negative matrix factorization (NMF) which is an unsupervised machine learning algorithm that extracts hidden patterns from unlabeled text data to create latent topics, and for topic classification, we considered classic approaches, neural networks, and pretrained algorithms. The Bidirectional Encoder Representations from Transformers( BERT), a pretrained model that uses an attention mechanism that learns contextual relations between words (or sub-words) in a text, and a Support Vector Machine (SVM) algorithm, a classic model which analyzes particular properties of learning within text data, record the best results for topic classification. Our results indicate that topic modeling and topic classification algorithms produce relatively similar results when topic classification algorithms are trained on a balanced dataset.
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    Machine Learning Analysis of Radio Data to Uncover Community Perceptions on the Ebola Outbreak in Uganda
    (ACM Journal on Computing and Sustainable Societies, 2024-09-16) Nakatumba-Nabende, Joyce; Mukiibi, Jonathan; Bateesa, Tobius Saul; Murindanyi, Sudi; Katumba, Andrew; Mutebi, Chodrine
    Radio is vital for people, especially in rural areas, to share their concerns through interactive talk shows. Understanding public perceptions of pandemics is crucial because they influence people’s attitudes and health-seeking behaviors. This study used machine learning to analyze English and Luganda radio broadcast data to understand public perceptions and perspectives on the Ebola outbreak in Uganda. Our findings revealed three main speaker categories: media personalities, community guests and listeners, and government officials. The government made the most significant effort to educate the public about the Ebola outbreak. The analysis showed that the community was hesitant to use Ebola vaccines, believing that they had not been tested on other populations where the Ebola virus had originated. The community was also concerned about the effects of the lockdown measures imposed during the COVID-19 pandemic. The analysis of the radio broadcast data revealed differences in the timing and content of the conversations between male and female speakers. These experiences can inform population-specific policies for handling ongoing and future pandemics.