## 1 Introduction  
Research has found language to be a reliable mode of measuring and understanding personality, and various methods of analysis have been devised to explore how personality is encoded in the way that people use language. In this sense, the affective computing community started to explore the valuable information encoded in social networks (SN) platforms as the platforms continuously collect individual affective intelligence such as habits, social interactions, and interests. The Big 5 personality model comprised of five super-traits, namely Extraversion, Neuroticism, Agreeableness, Conscientious, and Openness, is predominantly employed in many affective computing studies [1]. On other hand, the lack of interest of the community in investigating other significant personality systems such as the Psychoticism-Extraversion-Neuroticism (PEN) model impoverishes the body of knowledge. The PEN model was widely applied in criminology to study criminals' behaviour, especially in the context of Psychoticism [2, 3]. Nonetheless, it is tedious, expensive, and time-consuming to collect datasets aggregated to the PEN model due to the complexity of rules and regulations and the lack of confidence in the efficiency of feedback collection through self-reporting psychological instruments [4]. Because language and personality are strongly correlated, the use of seed words to categorize the topics according to human traits could be an alternative to overcome the inherent problems of collecting the personality-based datasets as the efforts to collect meaningful seed words are much cheaper and easier [5]. The recent literature also showed that incorporating seed knowledge to guide the auto-modeling is distributed in many aspects such as long documents [6], event detection and mapping [7], and unsupervised error estimation on various natural language text corpora [8]. At the same time, past studies have shown that dataless techniques practically infer the underlying latent semantical structure of data automatically by applying the probabilistic model and representing the features in the training documents as a probability distribution over the heterogeneity topics [9]. To the best of our knowledge, no dataless study has modeled the topics according to personality traits using seed words despite the significant influence of such auxiliary knowledge in the psychological study [10]. Thus, the proof-of-concept by [PERSON] et al. [10] and the potential of Latent Dirichlet Allocation (LDA) in modeling the dataless problem [11; 12; 13], inspired our team to hypothesize that a small set of seed words is enough to decipher the underlying psycholinguistic states encoded in texts and could leverage the automatic model to categorizes the texts according to personality traits. Therefore, our team experimented with the hypothesis by devising an unsupervised model called Seed-guided Latent Dirichlet Allocation (SLDA) to modeled the SN contents based on PEN model traits. To the best of our knowledge, this is the first PEN model textual corpus developed using an unsupervised model that can be used by other scholars for further empirical experiments. Because the affecting computing community has established many empirical studies on Extraversion and Neuroticism, this experiment also specifically focused on the representation of Psychoticism trait. Psychoticism is one of the canonical traits that conceptually defined the characteristic of bizarre thoughts and perceptions as well as typically correlated to criminal personalities and anti-social behaviour [3; 14]. This type of data-driven experiment on Psychoticism tended to shed light on the prospect of personality aspects in the forensic area, especially in the context of user trustworthiness [15; 16; 17]. The SLDA algorithm outperformed other topic models such as LDA and Latent Semantic Analysis. We also conducted comparative analysis extrinsically using several prominent off-the-shelf supervised classifiers to predict the ground-truth topics generated by SLDA.