## 3 Related Work
### Overview of the Preliminary Study  
This section briefly discusses the preliminary study conducted to compile the domain knowledge related to PEN model traits [22]. The motivation to compile the seed words aggregated to personality models was raised due to the representation power of such terms in revealing the demographical of the topical categories [5]. In this sense, we adopted a mechanism called Automatic Personality Perceptions (APP) and executed a survey to gather public perception towards the list of the sentiment words extracted from _myPersonality_ using Part-of-Speech Tagging elements. Under affective computing, APP refers to the identification of personality based on perception and typically focuses on predicting personality attributes based on observable behaviours such as the usage of words in writing [1; 23]. Even though APP concentrates on predicting the personality attributes based on observable behaviours, the general perception significantly indicates an individual's personality through social interaction [1; 23]. In the context of natural language, the semantic and emotional spectrum of Extraversion is directed towards positive emotionality that indicates the tendency of positive conversation with other people, whereas Neuroticism and Psychoticism connotated negative emotions [14] diversified based on the level of negativity [22]. In this experiment, we defined Neuroticism to depict the low-medium negativity whereas Psychoticism portrays medium-high negativity based on premises of semantic valence. Therefore, our team defined the psycholinguistics emotional scope by categorizing the low-medium negative intensity words to Neuroticism whereas medium-high negative intensities words corre \begin{table}
\begin{tabular}{c c} \hline \hline
**Trait** & **Characteristics** \\ \hline Extraversion & Sociable, lively, active, assertive, sensation seeking, carefree, dominant, surgent, and venturesome. \\ Neuroticism & Anxious, depressed, guilt feelings, low self-esteem, tense, irrational, shy, moody, and emotional. \\ Psychoticism & Aggressive, cold, egocentric, impersonal, impulsive, antisocial, unempathetic, creative, and tough-minded. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pen model traits and the corresponding characteristics [20]. sponded to Psychoticism as psychotics people are prone to intimidate or humiliate other individuals. According to the linguistic universal concept, the intense negative words function as a medium to express the intentions to abuse or humiliate other people [24]. In this preliminary study, we embraced the concept of sentiment valence to bridge the gap between linguistics and personality by measuring the semantical dimensionality embedded in words and associated them with each PEN model trait. To identify the valence, the publicly available valence-based rated English words corpus AFINN was adopted [25], and at the same time, a survey was conducted using words unavailable in AFINN to enrich the collection of seed words. The survey was performed to gather public perception towards the filtered words using valence as a sentiment-measurement metric. Throughout the survey, we collected 67 sentiment valence words such as mofo, cum, and Goddamnit and statistically associated them to each PEN model trait. In this experiment, Cronbach coefficient reliability scores of 0.951, 0.937, and 0.855 were achieved for Extraversion, Neuroticism, and Psychoticism, respectively [22]. The Cronbach coefficient measurement was adopted to statistically assess the internal consistency or reliability of sentiment valence metrics chosen by the respondents that assist summarization of overall perceptional dimensionality over the items (seed words) in the survey [26]. In this regard, the higher Cronbach coefficient values emphasized the validity of summarization and strengthen the correlation and degree of trustworthiness of public perceptions towards the items in the survey. Table 2 present some of the sentiment terms that will be used as seed words to model the topics according to PEN traits.