An evaluation of sentiment analysis frameworks in Nigerian indigenous language social media
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Background of the Study
Sentiment analysis frameworks have become integral in deciphering public opinions on social media, particularly for Nigerian indigenous languages that are rich in cultural nuance. These frameworks use natural language processing techniques to classify emotions expressed in digital texts. Nigerian indigenous languages, such as Yoruba, Igbo, and Hausa, pose unique challenges due to their tonal nature, idiomatic expressions, and code-switching phenomena (Olatunde, 2023). Recent research has focused on adapting sentiment analysis models to better capture the sentiment nuances embedded in indigenous expressions (Chinwe, 2024). Social media data provides an extensive corpus reflecting contemporary language use and public opinion, making it a valuable resource for sentiment analysis. However, the diverse linguistic structures and rapid evolution of online language require tailored approaches that consider local cultural and linguistic contexts. Enhanced sentiment analysis frameworks can inform policymaking, marketing strategies, and social research by providing real-time insights into public sentiment. The study explores various frameworks, comparing their performance and identifying specific challenges that arise when processing Nigerian indigenous language data in social media environments (Ibrahim, 2025).
Statement of the Problem
Although sentiment analysis is widely used in processing social media data, frameworks developed for Nigerian indigenous languages often underperform due to limited training data and the complexities of tonal and idiomatic expressions (Olatunde, 2023; Chinwe, 2024). Many existing models are optimized for major global languages and fail to accurately capture the emotional subtleties of indigenous languages. This results in misclassifications that undermine the reliability of sentiment analysis outputs. The gap between current technological capabilities and the linguistic realities of Nigerian indigenous languages necessitates a critical evaluation of existing frameworks and the development of customized approaches to improve accuracy and contextual relevance.
Objectives of the Study
- To evaluate the performance of current sentiment analysis frameworks on Nigerian indigenous language social media data.
- To identify linguistic challenges that affect sentiment classification in these languages.
- To propose improvements for developing more accurate sentiment analysis models.
Research Questions
- How effective are current sentiment analysis frameworks in processing Nigerian indigenous language texts?
- What linguistic factors contribute to sentiment misclassification?
- How can sentiment analysis models be improved for better accuracy in indigenous languages?
Significance of the Study
This study is significant as it assesses and refines sentiment analysis frameworks for Nigerian indigenous languages on social media. By identifying key challenges and proposing tailored solutions, the research will enhance the reliability of sentiment detection, thereby benefiting digital marketers, policymakers, and researchers. The study promotes more culturally and linguistically relevant data analysis, contributing to the broader understanding of public sentiment in Nigeria.
Scope and Limitations of the Study
This study focuses on sentiment analysis frameworks for Nigerian indigenous language social media data and does not address other computational linguistics applications or languages.
Definitions of Terms
- Sentiment Analysis: The computational process of determining the emotional tone behind textual content.
- Indigenous Languages: Native languages spoken by the original inhabitants of a region.
- Code-Switching: Alternating between two or more languages within a single conversation or text.
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