An assessment of computational models for processing Nigerian Pidgin in digital media
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Background of the Study
Digital media has transformed communication, offering platforms where languages, including Nigerian Pidgin, thrive in written and spoken forms. Computational models, such as natural language processing (NLP) systems, have been developed to analyze and process these languages. Nigerian Pidgin, widely used across Nigeria, presents unique challenges due to its dynamic grammar, evolving lexicon, and contextual variability. Recent advancements in NLP have started to incorporate Nigerian Pidgin, yet significant gaps remain in processing efficiency and accuracy (Ibrahim, 2023). The study explores computational models designed for Nigerian Pidgin, emphasizing their application in social media analytics, content moderation, and sentiment analysis. Given the rapid proliferation of digital content in Nigerian Pidgin, understanding how these models perform in real-world scenarios is vital. Integrating robust computational tools can lead to more accurate language processing, enabling better insights into user-generated content and facilitating digital inclusion (Adamu, 2024). The investigation will analyze model performance, adaptability to informal language, and potential for integration with digital media platforms.
Statement of the Problem
Although computational models for NLP have advanced considerably, those specifically designed for Nigerian Pidgin remain underdeveloped. Existing models often struggle with the language's fluid syntax and extensive lexical borrowing from multiple languages, leading to reduced accuracy in processing digital media texts (Ibrahim, 2023; Adamu, 2024). This limitation impedes effective sentiment analysis, automated moderation, and data mining of social media content, ultimately affecting the reliability of digital platforms that rely on linguistic analysis. A comprehensive evaluation of these models is needed to identify and address these shortcomings.
Objectives of the Study
- To evaluate the performance of current computational models in processing Nigerian Pidgin on digital media.
- To identify linguistic challenges that affect model accuracy and efficiency.
- To propose improvements for developing more robust NLP tools for Nigerian Pidgin.
Research Questions
- How effective are current computational models in processing Nigerian Pidgin digital media content?
- What linguistic features of Nigerian Pidgin present challenges for NLP systems?
- How can computational models be improved to better handle the complexities of Nigerian Pidgin?
Significance of the Study
This study will offer insights into the performance and limitations of computational models for Nigerian Pidgin, contributing to the development of more accurate NLP tools. The research benefits digital media analysts, developers, and linguists by improving language processing and supporting effective communication in Nigerian Pidgin.
Scope and Limitations of the Study
This study focuses on computational models for processing Nigerian Pidgin in digital media. It does not cover other languages or offline applications.
Definitions of Terms
- Computational Models: Algorithms and systems designed for processing and analyzing language data.
- Natural Language Processing (NLP): A field of artificial intelligence focused on the interaction between computers and human language.
- Digital Media: Electronic platforms that host and disseminate content online.
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