**4. Approaches to determine personality traits**

Different methodologies can be utilized for determining personality traits of an individual. One of the simplest approach is personality questionnaires where various personality inventories are developed in order to create concise psychological scales like- TIPI (Ten Item Personality Inventory), 28-item questionnaire, (BFI) Big Five Inventory which consists of 44 items, NEO PI, NEO PI-R personality inventory (the Revised NEO Personality Inventory) and NEO-FFI-3 (NEO Five Factor Inventory-3) [12, 15]. Apart from these questionnaires, social media usage questionnaire in the form of survey along with other approaches were also utilized by many researchers to determine personality traits.

Linear Discriminate Analysis (LDA), PCA (Principal Component Analysis), SVM (Support *vector machines*), Multinomial Naive Bayes and AdaBoost are some of the

most utilized classification algorithms for determining the text emotion. Linear Discriminate Analysis is a robust model used for classification and dimension reduction tool which can also be leveraged in data pre-processing. LDA assumes that data is distributed normally and each class has identical covariance matrices. As the classes are assumed to be linearly separable, multiple discrimination functions addressing several hyper-planes are made to recognize the classes [16]. AdaBoost is one of the most efficient ensemble methods that endeavor to approximate Bayes classifier by combining distinct weak classifiers after adding up the probabilistic predictions. It ultimately gives one single strong classifier. SAMME.R that is a variety of AdaBoost is used for multiclass classification problems [17, 18].
