An evaluation of machine learning applications in detecting sentiment in Yoruba tweets
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- NGN 5000
Background of the Study
Machine learning has become a cornerstone in sentiment analysis, particularly in social media contexts. Yoruba tweets, characterized by informal language, code-switching, and rich cultural expressions, present unique challenges for sentiment detection. Recent advancements in machine learning, including deep neural networks and ensemble methods, have been employed to analyze social media data and classify sentiment with increasing accuracy (Adewale, 2023). Researchers (Ogunleye, 2024) have noted that while these techniques perform well on standardized texts, the informal and dynamic nature of Twitter language in Yoruba necessitates specialized models. Furthermore, the frequent use of slang, idioms, and emoticons in tweets adds complexity to the sentiment analysis process. Emerging studies (Folake, 2025) highlight the need for incorporating linguistic and cultural features into machine learning models to improve sentiment classification. This study evaluates current machine learning applications in detecting sentiment in Yoruba tweets, examining their accuracy, challenges, and potential improvements, with the aim of enhancing automated sentiment analysis for social media analytics.
Statement of the Problem
Despite promising developments, machine learning models for sentiment analysis in Yoruba tweets often yield suboptimal results due to the informal nature of the text and cultural nuances (Adewale, 2023). Existing models, primarily trained on standardized datasets, struggle with slang, code-switching, and idiomatic expressions prevalent in social media. This leads to frequent misclassification and reduced reliability in sentiment detection (Ogunleye, 2024). The limitations in current approaches hinder effective social media monitoring and the extraction of actionable insights. There is a critical need to evaluate and refine these models, incorporating culturally relevant features to improve accuracy and robustness in sentiment analysis.
Objectives of the Study
- To evaluate the performance of machine learning models in detecting sentiment in Yoruba tweets.
- To identify challenges arising from informal language and cultural nuances.
- To propose improvements for integrating linguistic features into sentiment analysis models.
Research Questions
- How accurate are current machine learning models in classifying sentiment in Yoruba tweets?
- What informal linguistic features contribute to misclassification?
- How can models be enhanced to better capture cultural nuances?
Significance of the Study
This study is significant as it addresses the challenges of sentiment analysis in Yoruba tweets by evaluating machine learning applications and proposing culturally informed enhancements. Improved sentiment detection will benefit social media monitoring, market research, and public policy formulation by providing more reliable data. The findings will support the development of robust analytical tools that can accurately interpret the complex linguistic landscape of Yoruba on social media, benefiting researchers, developers, and policymakers.
Scope and Limitations of the Study
This study focuses on machine learning applications for sentiment analysis in Yoruba tweets and does not extend to other social media platforms or languages.
Definitions of Terms
- Sentiment Analysis: The process of determining the emotional tone behind textual data.
- Machine Learning: Algorithms that enable computers to learn from and make predictions on data.
- Yoruba Tweets: Social media posts written in Yoruba, often featuring informal language and code-switching.
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