EXPLORING INDUSTRY 4.0: SENTIMENT ANALYSIS ON BEHAVIORAL ADOPTION OF e-NAIRA DIGITAL CURRENCY IN NIGERIA USING SVM AND XGBOOST MODELS

Abstract

This research investigates the influence of Industry 4.0, focusing on eNaira as a key component of this revolution. It examines adoption trends through the application of advanced machine learning techniques, emphasizing supervised and ensemble learning models. Industry 4.0 refers to the fourth industrial revolution, marked by the integration of advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), robotics, big data, blockchain, and cloud computing into industrial processes. The eNaira is Nigeria’s national digital currency, designed to promote financial inclusion and advance the country’s cashless policy. Similar initiatives include eKrona in Sweden, eRupee in India, and eCNY in China. In this study, Support Vector Machines (SVM) and XGBoost are utilized to analyze public sentiment on eNaira adoption based on 1,254 entries from Twitter, Facebook, and Instagram, prominent platforms for public discourse. To ensure robust analysis, Natural Language Processing (NLP) techniques are applied to systematically clean, model, and interpret the data. Results indicate that while the SVM model achieved a respectable accuracy rate of 78%, XGBoost significantly outperformed it with an accuracy of 94%, demonstrating the ensemble method’s effectiveness in capturing distinct patterns in public sentiment toward eNaira. These insights provide valuable guidance for policymakers, offering a deeper understanding of public perceptions that can inform strategies to enhance the revolution of industry 4.0 and the eNaira adoption among citizens in Nigeria.