A Hybrid CNN-LSTM Model for Psychopathic Class Detection from Tweeter Users

In today’s digital era, the use of online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of textual content. The textual content that is produced by people reveals essential information regarding their personality, with psychopathy being among these distinct personality types. This work was aimed at classifying input texts according to the traits of psychopaths and non-psychopaths. Several studies based on traditional techniques, such as the SRPIII technique, using small-sized datasets have been conducted for the detection of psychopathic behavior. However, the purpose of the current study was to build an effective computational model for the detection of psychopaths in the domain of text analytics and computational intelligence. This study was aimed at developing a technique based on a convolutional neural network + long short-term memory (CNN-LSTM) model by using a deep learning approach to detect psychopaths. A convolutional neural network was used to extract local information from a text, while the long short-term memory was used to extract the contextual dependencies of the text. By combining the advantages of convolutional neural network and long short-term memory, the proposed hybrid CNN-LSTM was able to yield a good classification accuracy of 91.67%. Additionally, a large-sized benchmark dataset was acquired for the effective classification of the given input text into psychopath vs. non-psychopath classes, thereby enabling persons with such personality traits to be identified.