BeWell: An Integrated Mental Health Application Using LSTM Neural Network Model and Vader Sentiment Analysis for Emotional Well Being

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Abstract

The burgeoning field of mental health has significantly benefited from advancements in Artificial Intelligence (AI), particularly in emotion recognition and analysis. This paper presents the "BeWell" application, a pioneering tool designed to leverage the synergy between Long Short-Term Memory (LSTM) neural networks with the proposed Emotional Support System (EMS) algorithm and Vader sentiment analysis for enhanced mental health support. The application aims to provide users with an interactive platform for emotion tracking and mental well-being assessment, utilizing vocal and textual inputs. By analyzing speech patterns and text input, "BeWell" identifies emotional states and provides tailored responses and recommendations. We detail the application's development, emphasizing its robust AI-driven backend, which combines LSTM-EMS models known for their efficacy in sequence prediction tasks with the nuanced sentiment detection capabilities of the Vader algorithm. Our experimental results demonstrate the system's precision and reliability in real-world scenarios, offering a versatile and user-friendly approach to managing mental health. The "BeWell" application stands as a testament to the potential of integrating multiple AI techniques to create sensitive and adaptive mental health technologies.

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Keywords

Artificial Intelligence, BeWell, Neural Network Model, Confusion Matrix, Emotion Classification, Machine Learning, Long Short Memory Model, Vader Sentiment,