## 3 Related Work
### Dataless Topic Modeling  
Topic modeling techniques such as LDA capture substantial inter-intra structures of documents using statistical distribution [9; 40]. LDA had been acknowledged as one of the successful mechanisms to model the topics according to the document themes [40]. Dataless modeling is an unsupervised approach that does not require any labeled document, whereby the models penetrate the semantic similarity encapsulated between a given document and a set of predefined classes to determine the categories of the given document [5]. This technique has become an attractive approach as it can reduce the time and human effort needed to label the training document besides training without annotated data. [PERSON] et al. [41] have proposed the Descriptive LDA (DescLDA) to model the topics by adjusting the Dirichlet Priors parameter (\(\beta\)) and other hyperparameters without injecting prior external knowledge. [PERSON] et al. [12] proposed the Laplacian Seed Word Topic Model (LapSWTM) that exploits local neighbourhood structures and enhances the capturing of discriminative features. The LapSWTM has revealed that the use of prior knowledge or seed words substantially improves the model to better converge in an unsupervised manner. On the other hand, [PERSON] et al. [42] proposed the Guided Non-Negative Matrix Factorization (GNMF) model that could incorporate the external seed words to model the topics according to predetermined topics. Theoretically, GNMF is based on a linear-algebraic optimization algorithm called Non-Negative Matrix Factorization (NMF) that used the matrix factorization technique to explore the high dimensional data and automatically extracts sparse and meaningful features from a set of nonnegative data vectors. Although the explicit use of seed words to identify the topics has been performed previously [10; 13], the tendency of using such a guided approach to model the topics in the contexts of psycholinguistics has not been investigated. Therefore, the research gap inspired our team to proposed a seed guided model to automatically label the social networks messages based on personality traits. This idea is practical because the seed words compiled earlier can be prior intelligence to model the topics in an unsupervised manner using dataless Seed Guided LDA (SLDA). The are several reasons to proposed SLDA to categorize the documents based on PEN model traits. First, it is infeasible to use the conventional LDA that exploits the word co-occurrence on diversified datasets [11]. Second, the SLDA is more robust than conventional LDA as the guided model could assist the topic to discover secondary or nondominant statistical patterns in the document. Third, the type/number of the categories embedded in the document were known, and the availability of the corresponding domain knowledge could reduce the bottlenecks caused by manually collecting and labeling the document [5]. The premises of this study also differ from existing dataless studies where those experiments automatically generate the seed words based on statistics without considering the psychological themes embedded in the texts [43; 44], guide the model to identify other relevant attributes or determined the number of topics [7; 8; 45], and merely applied to assist the topic outputs [42]. In Psychology, emotion and perception were quintessential characteristics that naturally formed the personality of a human being [10]. Thus, identifying psycholinguistics related seed words automatically may mislead the categorization and lead to the noisiness in the topic generation process as well as widen the semantics and contextual gap [42]. Another key difference between manual and automatic seed selection is the latter approach typically will grow the small set of seed words into a much larger set [43], whereas our experiments showed that the proposed algorithm performed well when the initial set of seed words reduced gradually on each run.