A Personalized Content Filter for Mental Health Apps Using BiLSTM

Mental health applications have gained significant popularity, but many still fall short in delivering personalized emotional support. This study explores a Personalized Content Filter powered by BiLSTM to analyze user-generated text, such as journal entries or chat messages, and identify emotions like sadness, anxiety, joy, and calmness. By determining the prevailing emotion, the system provides customized recommendations, such as mindfulness practices, stress-relief techniques, or uplifting content.The BiLSTM model uses contextual insights from text to ensure precise emotion recognition, employing pre-trained word embeddings for robust feature extraction. Over time, the system learns from individual users’ emotional trends, refining its recommendations to suit their specific needs. Experimental results confirm the model’s accuracy and reliability, emphasizing its capability in emotion detection.Incorporating this system into mental health applications has the potential to boost user engagement while offering timely support for emotional well-being. This research underscores the potential of deep learning in advancing mental health tools and suggests future directions, such as integrating multimodal data and exploring broader real-world implementations.