CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms

Depression is a serious mental health condition that affects a person’s ability to feel happy and engaged in activities. The COVID-19 pandemic has led to an increase in depression due to factors such as isolation, financial stress, and uncertainty about the future. Additionally, restrictions on travel and socializing have contributed to feelings of loneliness and isolation. In this research, we present a deep learning framework named CoDeS (COVID-caused depression symptoms) for detecting prodromes of depression in online users caused due to COVID pandemic. This framework uses a combination of CNN, LSTM, and integrated CNN-LSTM techniques, with three different feature representation methods, viz. Word2Vec, TF-IDF, and BERT. Nine experiments were conducted on individual and integrated models, and the results were evaluated based on the accuracy, precision, recall, F1-score, and Matthews correlation coefficient (MCC) performance metric. The highest accuracy value of 98.95% was recorded for the TF-IDF-based integrated CNN+LSTM model. When the same integrated model was trained using Word2Vec and BERT-based features, it still performed well with an accuracy of 97.32% and 98.51% respectively. The results demonstrate that the TF-IDF-based feature representation performed better than the Word2Vec and BERT-based feature representations for the CNN and LSTM models in identifying COVID-caused depression symptoms. The proposed approaches showcased substantial advancements over the existing ones, with significant improvements in accuracy. TF-IDF-CNN+LSTM achieved an accuracy approximately 37.28% higher, while BERT-CNN, BERT-LSTM, and BERT-CNN+LSTM achieved accuracy enhancements of approximately 29.78%, 34.44%, and 27.14% respectively. These accuracy improvements demonstrate the superior classification capabilities of the proposed approaches, leading to more precise depression analysis outcomes. In terms of F1 measure, the proposed approaches consistently demonstrated superior performance, with F1 measure values ranging from 0.965 to 0.987. BERT-CNN+LSTM achieved the highest F1 measure, highlighting its balanced precision and recall. Overall, the proposed approaches outperformed existing ones in terms of recall, precision, accuracy, and F1 measure, with improvements ranging from 27.14 to 44.85%. By incorporating advanced techniques such as TF-IDF, CNN, LSTM, and BERT, more accurate and reliable sentiment analysis outcomes can be achieved, offering the potential for enhanced applications in this field.