Analyze and Implement a Reinforced AI Chatbot in Guayaquil to Improve Mental Health in Adolescents with the Use of the Neural Generative Models

Mental health is vital to the development of young adolescents to create strong relationships and resilience, keeping a positive influence on society. The impact of COVID-19 on mental health is anticipated to be significant to the population, especially adolescents by depriving social contact and creating mental disorders. Currently, there are countless Chatbots that give dynamic chatbot services in health. In recent years, neural networks for natural language processing (NLP) have shown to generate more responses learned by the machine. This study aims to implement and compare two Chatbot mobile applications using Neural generative with sentiment models: OpenAI GPT-3 model, and personalized chatbot based on deep Learning, Transformer BERT, and TextBlob model. The dataset for this model was generated from a database of the flow conversation from GPT-3 and the trained bot, frequently asked questions collected at the beginning and end of the term of academic. To analyze, the sentiment it trains the dataset used in the conversation which validates the prediction through a confusion matrix which resulted in test accuracy frequently correct is 70% for Transformer Bert and 68% in TextBlob. To test the usability of the chatbot and application, a survey was conducted on a group of 30 participants, using Chatbot Usability Questionnaire (CUQ) and Linkert scale, it showed that GPT-3 CUQ Mean is 77,71 higher than Deep Learning. As for the Linkert scale, it was verified that 68% of participants perceived that the chatbot was adequate to their concerns, as well that the acceptance rate was 90%.