A Multimodal Deep Learning Approach for Identification of ‎Severity of Reflective Depression ‎ - Hanen Karamti; Eatedal Alabdulkreem; Hedia Zardi; Abeer M Mahmoud (IAES Indonesian Section, 2022)
Social media consumes a greate time of our dialy times that generate a significant amount of information through expressing feeling and activities, sharing admiral contents, viewing, and more. This information mostly contains valuable discoveries. Despite many attempts to mining such produced data, it is still unexploited in certain issues and attracts many research areas. In this paper, we use the data extracted from social media from female’s pages to detect possibility of depression. A new deep learning model based on the psycholinguistic vocabulary to create the embedding words is developed. First, we extract the features from the data before and after the preprocessing phase. Second, the Convolutional Neural Network (CNN) is used to label the data for extracting the remaining features. Based on the previouse two phases; the developed model succeeded to predict the depression possibilty. Adetailed comparative analysis is also presented for the evaluation of the proposed system. The proposed indicator model proved promising results in predicting depression.