Deep Learning for Detecting Depression: Unveiling Emotional Distress from Tweets

In the age of social media, detecting depression through user messages poses challenges due to recognition and trends. However, this project focuses on constructing a deep learning model using Twitter data to predict depression based on users' tweets. The current approaches like RNNs, LSTMs are inefficient in extracting contextual and sequential information from data in low compute environments. The solution proposed aims to incorporate the upsides from both CNNs and LSTMs to better extract features. Firstly, Twitter data is collected to identify relevant linguistic features for depression detection. Carefully characterized features are selected to capture subtle nuances in the content. Secondly, a comprehensive analysis is conducted, considering correlation significance, hidden topics, and word frequencies extracted from the text, which play a vital role in depression detection. These analyzed features are then fed into pre-trained CNN, LSTM, and RNN models. The results indicate that the RNN model outperforms the CNN-LSTM model, achieving an exceptional accuracy rate of 99.29% in detecting depression from posted tweets. By utilizing remembering networks and deep learning classifiers, the model can effectively classify, analyze, and predict data, providing insights into users' mental health. This research highlights the potential of using social media data and deep learning techniques to gain valuable insights into individuals' emotional well-being, ultimately leading to better mental health support and intervention strategies.