Depression Detection Using Deep Learning and Natural Language Processing Techniques: A Comparative Study

Depression is a frequently underestimated illness that significantly impacts a substantial number of individuals worldwide, making it a significant mental disorder. The world today lives fully connected, where more than half of the world’s population uses social networks in their daily lives. If we interpret and understand the feelings associated with a social media post, we can detect potential depression cases before they reach a major state associated with consequences for the patient. This paper proposes the use of natural language processing (NLP) techniques to classify the sentiment associated with a post made on the Twitter social network. This sentiment can be non-depressive, neutral, or depressive. The authors collected and validated the data, and performed pre-processing and feature generation using TF-IDF and Word2Vec techniques. Various DL and ML models were evaluated on these features. The Extra Trees classifier combined with the TF-IDF technique emerged as the most successful combination for classifying potential depression sentiment in tweets, achieving an accuracy of 84.83%.