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.

在当今的数字时代，对在线社交媒体网络（例如Google，YouTube，Facebook和Twitter）的使用使人们能够生成大量的文本内容。人们产生的文字内容揭示了有关其人格的基本信息，其中心理疾病就是这些不同的人格类型。这项工作旨在根据心理变态者和非心理变态者的特征对输入文本进行分类。已经进行了一些基于传统技术（例如SRPIII技术）的研究，这些研究使用小型数据集来检测精神病患者的行为。但是，当前研究的目的是建立一个有效的计算模型，以检测文本分析和计算智能领域中的精神病患者。这项研究旨在通过使用深度学习方法检测精神病患者，开发基于卷积神经网络+长短期记忆（CNN-LSTM）模型的技术。卷积神经网络用于从文本中提取局部信息，而长短期记忆则用于提取文本的上下文相关性。结合卷积神经网络和长短期记忆的优点，提出的混合CNN-LSTM能够获得91.67％的良好分类精度。此外，还获取了一个大型基准数据集，以将给定的输入文本有效地分类为 卷积神经网络用于从文本中提取局部信息，而长短期记忆则用于提取文本的上下文相关性。结合卷积神经网络和长短期记忆的优点，提出的混合CNN-LSTM能够获得91.67％的良好分类精度。此外，还获取了一个大型基准数据集，以将给定的输入文本有效地分类为 卷积神经网络用于从文本中提取局部信息，而长短期记忆则用于提取文本的上下文相关性。结合卷积神经网络和长短期记忆的优点，提出的混合CNN-LSTM能够获得91.67％的良好分类精度。此外，还获取了一个大型基准数据集，以将给定的输入文本有效地分类为精神病患者vs.非精神病患者，因此可以识别具有这种人格特质的人。