An appraisal of machine learning techniques in predicting Yoruba language usage trends in Ibadan
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Background of the Study
Machine learning has revolutionized the way linguistic data is analyzed, particularly in predicting language usage trends. In Ibadan, where Yoruba is widely used across media, education, and everyday communication, machine learning techniques offer novel insights into emerging linguistic patterns and usage shifts. These techniques analyze large datasets from social media, digital publications, and spoken language corpora to identify trends, predict future usage, and detect shifts in vocabulary and syntax (Olawale, 2023). By applying supervised and unsupervised learning models, researchers can forecast changes influenced by factors such as urbanization, globalization, and technology adoption (Adesina, 2024). Furthermore, sentiment analysis and topic modeling help uncover cultural and social factors affecting language usage, providing data-driven support for language planning and policy formulation (Fashina, 2025). However, the unique linguistic features and informal usage patterns of Yoruba in Ibadan require model adaptations to capture local variations accurately. This study examines the current state of machine learning applications in predicting Yoruba language usage trends and evaluates their effectiveness in informing educational and cultural strategies.
Statement of the Problem
Despite the promise of machine learning in forecasting language trends, models applied to Yoruba in Ibadan often fall short due to the informal nature of everyday language and limited annotated datasets. Existing techniques sometimes misinterpret code-switching and colloquial expressions, leading to inaccurate predictions (Olawale, 2023; Adesina, 2024). This inadequacy hinders language planning and policy efforts, making it imperative to refine predictive models to better capture the nuances of Yoruba usage in urban contexts.
Objectives of the Study
- To evaluate the effectiveness of current machine learning techniques in predicting Yoruba usage trends.
- To identify challenges specific to modeling informal Yoruba language patterns.
- To propose enhancements for improving prediction accuracy in the Ibadan context.
Research Questions
- How effective are current machine learning models in predicting Yoruba language usage trends in Ibadan?
- What challenges do informal language features present in model prediction?
- How can predictive models be enhanced to better reflect local linguistic variations?
Significance of the Study
This study is significant because it harnesses machine learning to forecast Yoruba language trends, offering insights that can inform language policy and educational strategies in Ibadan. By addressing current challenges, the research will support cultural preservation and guide technological advancements in linguistic research, benefiting both academic and community stakeholders.
Scope and Limitations of the Study
This study focuses on machine learning techniques applied to predicting Yoruba language usage trends in Ibadan. It does not cover other cities or alternative predictive methods.
Definitions of Terms
- Machine Learning: Algorithms that learn from data to make predictions.
- Language Usage Trends: Patterns and shifts in the way a language is used over time.
- Yoruba Language: A major indigenous language of Nigeria, particularly spoken in Ibadan.
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