Automated Text-based Depression Detection using Hybrid ConvLSTM and Bi-LSTM Model

Depression and its symptoms are very common disorders of mental health. They affect the day-to-day activity of the person and degrade the quality of life. The article presents the comparative study of different deep learning models on natural language processing data for detection of depression using textual data. Several studies have been performed for depression detection using artificial intelligence and deep learning state of the art methods. This article investigates the state-of-the-art models and perform hyperparameter tuning for best accuracy results and develop our own hybrid model for detection of depression with improved accuracy scores. The aim of our study is to research and compare the existing findings in deep learning and machine learning models for depression detection and build a precise hybrid model for depression detection with higher accuracy and scores.