Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

In this paper, we propose an approach based on artificial intelligence (AI) and text mining techniques for measuring the degrees of appearance of symptoms related to paranoid disease in Twitter users. This operation will then help in the detection of people suffering from paranoid personality disorder in a manner that provides justifiable and explainable results by answering the question: What factors lead us to believe that this person suffers from paranoid personality disorder? These challenges were achieved using a deep neural approach, including: (i) CNN layers for features extraction step from the textual part, (ii) BiLSTM layer to classify the intensity of symptoms by preserving long-term dependencies, (iii) an SVM classifier to detect users with paranoid personality disorder based on the degree of symptoms obtained from the previous layer. According to this approach, we get an F-measure rate equivalent to 71% for the average measurement of the degree of each symptom and 65% for detecting paranoid people. The results achieved motivate and encourage researchers to improve them in view of the relevance and importance of this research area.