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One of the most prevalent mental disorders, i.e., depression, affects millions of individuals worldwide. Most people who are depressed are hesitant to reveal their condition to others. The main objective of this study is to predict the depression of online users using Natural Language Processing (NLP) tools and deep learning techniques. This paper proposes a approach focusing sentiment classification which is a hybrid of Long Short-Term Memory (LSTM) and Temporal Convolution Network (TCN) models. The proposed LSTM-TCN model addresses the limitations of traditional Convolutional Neural Networks (CNNs), i.e., the inability of CNNs to fully capture text features during feature extraction. Furthermore, a single model cannot properly extract deep text features. A publicly available large-scale dataset comprising Twitter data is utilized for experimentation purposes. A comparison analysis is performed illustrating …