Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention

Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers to overcome the depression text categorization problem. The networks distribute weight to each node in a neighborhood based on neighbors’ properties/emotions without using expensive matrix operations like similarity or architectural knowledge. This study expands the emotional vocabulary through the use of hypernyms. As a result, our architecture outperforms the competition. Our experimental results show that the emotion lexicon combined with an attention network achieves receiver operating characteristic (ROC)-0.87 while staying interpretable and transparent. After obtaining qualitative agreement from the psychiatrist, the learned embedding is used to show the contribution of each symptom to the activated word. By utilizing unlabeled forum text, the approach increases the rate of detecting depression symptoms from online data.