Intelligent Mental Depression Recognition Model with Ensemble Learning Through Social Media Tweet Resources

Abstract

Mental disorders are major problems and influence the thought of suicide, so it needs to be diagnosed and treated earlier. Detecting the symptoms of mental disorders is highly significant as they are considered a life-threatening task. Hence, this paper plans to implement new mental depression recognition by analyzing the tweet media text to secure the lives of the affected individuals. In the standard dataset, the text tweet data is collected. The gathered text data are used in the bags of n-grams and glove embedding for extracting the text features. Here, the extracted features are considered for the weighted feature selection, where the weight optimization takes place using the Awareness Probability-based Crow Black Widow Search algorithm. Finally, the weighted features are used for the recognition phase using the Convolutional Neural Network-Ensemble learning, where the final layer of CNN is replaced by the “Support Vector Machine (SVM), Random Forest (RF), Long Short Term Memory (LSTM) and Artificial Neural Network (ANN)” for classification. Here, parameter optimization occurs in the classifiers to improve the evaluation of the designed method. The experimental outcomes show the efficient performance of the developed method when compared with the other baseline approaches.