An appraisal of computational linguistic methods in processing Nigerian Pidgin social media data
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Background of the Study
The rapid expansion of digital communication has led to an increased presence of Nigerian Pidgin on social media platforms, prompting a need for robust computational linguistic methods to process and analyze this dynamic language. Over recent years, researchers have employed techniques such as part-of-speech tagging, named entity recognition, and sentiment analysis to understand the linguistic features of Nigerian Pidgin texts (Adeleke, 2023). Given its informal structure and frequent code-switching, computational approaches must address challenges like non-standard orthography and rapid lexical evolution. Moreover, social media data provides a rich corpus reflecting everyday language use, which is vital for creating adaptive language models that can learn from real-time interactions (Okoro, 2024). This study examines the efficiency of current computational techniques and explores innovative algorithms that integrate machine learning and deep learning models to enhance processing accuracy. It considers how context, slang, and cultural references are handled by these systems. Researchers have noted that the unique characteristics of Nigerian Pidgin require specialized pre-processing methods to improve text mining results (Babatunde, 2025). Thus, the study reviews recent developments in computational linguistics and assesses their applicability to the diverse and rapidly evolving digital content produced in Nigerian Pidgin.
Statement of the Problem
Despite advancements in computational linguistics, processing Nigerian Pidgin on social media remains challenging due to its non-standardized grammar, lexical borrowing, and frequent code-switching. Current models often yield inaccurate results when applied to informal texts, limiting their usefulness for linguistic analysis and real-time applications (Adeleke, 2023; Okoro, 2024). Moreover, a lack of large-scale annotated corpora hampers the development of robust language models. This gap in processing capability restricts the effectiveness of sentiment analysis, content moderation, and trend detection, ultimately impacting research and digital policy formulation. A systematic evaluation of existing methods is required to identify shortcomings and propose enhancements tailored to Nigerian Pidgin’s unique linguistic landscape.
Objectives of the Study
- To evaluate the performance of current computational linguistic methods in processing Nigerian Pidgin social media data.
- To identify specific challenges and limitations in analyzing non-standardized texts.
- To propose enhanced algorithms and pre-processing techniques tailored for Nigerian Pidgin.
Research Questions
- How effective are current computational methods in processing Nigerian Pidgin social media texts?
- What challenges do these methods face with respect to non-standard language features?
- How can computational models be improved to better analyze Nigerian Pidgin data?
Significance of the Study
This study is significant as it addresses the gap in computational processing of Nigerian Pidgin on social media, providing insights that can improve natural language processing applications. By identifying limitations in current models and proposing tailored solutions, the research supports better sentiment analysis, trend detection, and content moderation. The outcomes will benefit developers, linguists, and digital policymakers in creating more inclusive language technologies that reflect the unique dynamics of Nigerian Pidgin.
Scope and Limitations of the Study
This study focuses solely on computational linguistic methods for processing Nigerian Pidgin social media data. It does not extend to other digital communication forms or languages.
Definitions of Terms
- Computational Linguistics: The field that uses computer algorithms to process and analyze human language data.
- Social Media Data: User-generated content from digital platforms.
- Non-standard Orthography: Spelling conventions that deviate from standardized norms.
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