DEVELOPMENT OF A SENTIMENT ANALYSIS- DRIVEN PREDICTIVE AI MODEL FOR DETECTING SUICIDE IDEATION IN NIGERIAN SOCIAL MEDIA PLATFORM

Abstract

Suicidal thoughts among youths are a growing concern, particularly in low-income nations like Nigeria where underreporting, stigma, and a lack of mental health facilities make early intervention challenging. Since many people now use different social media platforms to express their psychological suffering online, artificial intelligence has created proactive potential to discover mental health issues. In order to identify early indicators of suicide intentions on Nigerian social media, this study develops a predictive AI algorithm that employs sentiment analysis. 52,000 publicly available Nigerian social media posts collected between 2020 and 2025 were manually classified into risk categories for suicidal ideation and non-suicidal ideation using a supervised machine learning approach. This study takes into account linguistic characteristics unique to Nigeria that are often overlooked in Western dataset models, such as code-switching, local slang, and Nigerian Pidgin. It employed a comprehensive preprocessing method, sentiment feature extraction, and contextual embeddings. Additionally, the performance of a transformer-based BERT model that was modified for classification was evaluated using accuracy, precision, and recall metrics. The results showed that the model had 96.7% accuracy, 88.4% precision, and 92% recall in identifying both overt and covert signs of suicidal ideation. The results show that in linguistically difficult situations, sentiment-aware deep learning models perform better than traditional methods. This paper provides a culturally appropriate AI framework for mental health monitoring in Nigeria and lays the groundwork for early-warning systems that facilitate prompt intervention, policy creation, and suicide prevention campaigns.