Leveraging BiLSTM for Deep Learning-Based Mental Health Chatbots
DOI:
https://doi.org/10.33474/infotron.v5i1.23242Keywords:
Chatbots, Mental Health, BiLSTM, Deep Learning, ClassificationAbstract
The high prevalence of mental health issues and limited access to professional information and support have driven the search for innovative solutions. One promising approach is the development of chatbot systems that provide quick and accessible mental health information. This study evaluates the performance of the Bidirectional Long Short-Term Memory (BiLSTM) algorithm in identifying and classifying user inputs within a mental health chatbot system. BiLSTM is chosen for its ability to process sequential data in both directions, allowing it to capture context more effectively than unidirectional models and better understand user intent. Deep learning methods like BiLSTM have also demonstrated higher accuracy compared to traditional machine learning models. This study focuses solely on BiLSTM to evaluate its performance in this context. The mental health dataset used in this study was sourced from previous research published on the GitHub platform and contains 100 classes of mental health-related questions and statements. This dataset was used to train the BiLSTM model to recognize user intent and generate relevant responses. The model achieved 98% accuracy on the training data. For evaluation on the test set, a confusion matrix was used, yielding an accuracy of 82%. The chatbot is implemented as a web-based application using a Python framework and is designed to provide users with insights and knowledge through text-based interactions. These results highlight the potential of the BiLSTM-based chatbot system to deliver effective and efficient mental health information services
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