Modified arithmetic optimization algorithm with Deep Learning based data analytics for depression detection

Depression detection is the procedure of recognizing the individuals exhibiting depression symptoms, which is a mental illness that is characterized by hopelessness, feelings of sadness, persistence and loss of interest in day-to-day activities. Depression detection in Social Networking Sites (SNS) is a challenging task due to the huge volume of data and its complicated variations. However, it is feasible to detect the depression of the individuals by examining the user-generated content utilizing Deep Learning (DL), Machine Learning (ML) and Natural Language Processing (NLP) approaches. These techniques demonstrate optimum outcomes in early and accurate detection of depression, which in turn can support in enhancing the treatment outcomes and avoid more complications related to depression. In order to provide more insights, both ML and DL approaches possibly offer unique features. These features support the evaluation of unique patterns that are hidden in online interactions and address them to expose the mental state amongst the SNS users. In the current study, we develop the Modified Arithmetic Optimization Algorithm with Deep Learning for Depression Detection in Twitter Data (MAOADL-DDTD) technique. The presented MAOADL-DDTD technique focuses on identification and classification of the depression sentiments in Twitter data. In the presented MAOADL-DDTD technique, the noise in the tweets is pre-processed in different ways. In addition to this, the Glove word embedding technique is used to extract the features from the preprocessed data. For depression detection, the Sparse Autoencoder (SAE) model is applied. The MAOA is used for optimum hyperparameter tuning of the SAE approach so as to optimize the performance of the SAE model, which helps in accomplishing better detection performance. The MAOADL-DDTD algorithm is simulated using the benchmark database and experimentally validated. The experimental values of the MAOADL-DDTD methodology establish its promising performance over another recent state-of-the-art approaches.