A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Depression is a severe mental health issue. The user-generated content on social media (SM) is growing nowadays. Some computational approaches have been proposed for detecting depression based on users' SM data. However, because of the use of formal language, short range of words and misspellings in the SM data, depression detection (DD) is a challenging task. This paper proposes a novel deep learning (DL) technique for performing DD of the SM data with the help of the hybrid feature selection (FS) mechanism. Initially, two publicly available datasets containing user tweets are collected for implementing the proposed research model. Then the collected datasets are preprocessed for further processing. The preprocessing phase includes critical processes that contribute to creating a ready-to-use dataset for training and testing. After preprocessing, the preprocessed data is divided into prime and non-prime words based on the dictionary approach. After that, the hybrid FS approach is implemented to select the most relevant features from the prime and non-prime words for higher classification accuracy (AC). In the hybrid model, firstly Term Frequency Inverse Document Frequency integrated Modified Information Gain (TFIDF-MIG) approach is proposed that assigns the score value of each prime and non-prime word in the dataset. Secondly, optimal features are selected from the weighted features using the Improved Elephant Herding Algorithm (IEHA). Finally, the decided features from the hybrid model are fed into the DL model, namely attention included improved ReLU-based Convolution Neural Network with Long Short-Term Memory (AIRCNN-LSTM) for DD. Experiments are performed on the collected datasets to assess the proposed model's performance efficiency. The results of the extensive experiments show that the presented work outperforms existing techniques regarding DD classification AC by locating the best solutions. At the same time, it reduces the number of features chosen.