An Evaluation of Automated Sentiment Analysis in Nigerian Social Media for Hausa Language
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- NGN 5000
Background of the Study
Automated sentiment analysis has become a vital tool for interpreting public opinion on social media platforms. In Nigeria, social media users often express opinions in Hausa—a major indigenous language—using informal language and code-switching (Ogunleye, 2023). Advanced sentiment analysis models utilize machine learning algorithms to detect emotions and attitudes in text. However, the informal and context-dependent nature of Hausa social media posts presents unique challenges, including idiomatic expressions, mixed language use, and non-standard orthography (Adejumo, 2024). Recent studies have demonstrated that sentiment analysis tools designed for global languages often underperform when applied to Hausa data, leading to misclassification and reduced reliability (Ibrahim, 2025). This study evaluates current automated sentiment analysis systems in the context of Hausa social media, examining their accuracy and limitations, and proposes strategies for improving their performance through enhanced linguistic models and culturally relevant training data.
Statement of the Problem
Despite the growing use of sentiment analysis tools, their application to Hausa social media data faces significant challenges. Many automated systems struggle to accurately interpret the informal, idiomatic, and code-switched nature of Hausa expressions, leading to frequent misclassification of sentiment (Ogunleye, 2023). The lack of robust annotated Hausa corpora further limits the performance of these systems, impacting research and decision-making based on social media analytics (Adejumo, 2024).
Objectives of the Study
- To evaluate the performance of automated sentiment analysis tools on Hausa social media texts.
- To identify linguistic and cultural challenges that hinder accurate sentiment detection.
- To propose improvements for adapting sentiment analysis models to Hausa.
Research Questions
- How effective are current sentiment analysis tools in processing Hausa social media posts?
- What specific linguistic features challenge sentiment classification in Hausa?
- How can these tools be optimized to better capture the nuances of Hausa expression?
Significance of the Study
This study is significant as it addresses the challenges of applying automated sentiment analysis to Hausa social media, offering insights to enhance NLP tools for indigenous languages. Improved sentiment analysis will support better understanding of public opinion and more informed decision-making by businesses, policymakers, and researchers, ultimately contributing to more culturally relevant digital analytics.
Scope and Limitations of the Study
The study focuses on automated sentiment analysis tools for Hausa social media and does not extend to other languages or offline sentiment analysis methods.
Definitions of Terms
- Sentiment Analysis: The computational process of determining the emotional tone behind text.
- Automated Tools: Software systems that perform language analysis with minimal human intervention.
- Hausa Language: A major indigenous language spoken in Northern Nigeria.
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