Browsing by Author "Samuel Ejomafuvwe LUCKY"

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    A Web Based Chatbot for Mental Health Support
    (Lead City University, Ibadan, 2023-12) Samuel Ejomafuvwe LUCKY
    Despite the significance attributed to mental health, a considerable number of individuals have difficulties in accessing prompt and tailored mental health interventions. This predicament can be attributed to various factors, including societal stigmatisation, limited availability of resources, and residing in geographically isolated areas. This study addresses the persistent challenge of providing timely and individualized mental health treatment through the development of a web-based chatbot for personalized therapy. The study comprises the utilisation of a dataset containing frequently asked questions (FAQs) related to mental health. Preprocessing techniques, including lemmatization, lowercasing, and duplication removal, are employed in order to prepare the data for analysis. The machine learning model, which utilises neural networks, undergoes training and has a negative association between epochs and loss magnitude, suggesting enhanced performance as the training progresses. The findings indicated that the developed chatbot demonstrated a high level of proficiency in delivering personalised mental health care that is relevant to the individual, providing fast responses, and offering appropriate recommendations for therapy. Additionally, the user feedback received during the performance evaluation highlights a high level of satisfaction and a strong inclination to utilise the chatbot again in the future. The study highlights the potential of chatbots, particularly those based on LSTM architecture in effectively addressing mental health issues and enhancing the availability of resources. The study therefore recommends that continuous improvement refining and enhancing the chatbot's capabilities by regularly updating the chatbot's knowledge base, therapy recommendations, and conversational abilities to ensure it remains relevant and effective. Keywords: Epochs, Frequently asked questions (FAQs), Lemmatization, Lowercasing, LSTM architecture, Machine learning model, Mental health, Personalized therapy Word Count: 247 Words