Extraversion Personality Classification Using Machine Learning from Linguistic Features

Abstract

Personality is a principal premise of human behavior. At generally essential, personality including examples of thought, feeling, behaviors that make an individual novel. Personality will straight forwardly or in a roundabout way impact the collaboration or inclinations of an individual. This research utilizing different learning algorithms and ideas of data mining to mine on the data features and gain from the example. The point of this analysis is to investigate various choices of the algorithm on altering the personality prediction source code by utilizing logistic regression algorithm and to discover whether the accuracy of the characterization can be improved. There are five characteristic of various individuals that are known as the Big Five characteristic, which is openness, neuroticism, conscientiousness, agreeableness and extraversion that have been put away in the dataset utilized for preparing. At that point, an outline and comparison will be provided on the various measures taken to decrease the issues faced by researchers in this field. Classification methods executed are Support Vector Machine, K Nearest Neighbor, Naïve Bayes, Logistic Regression, Decision Tree, and Random Forest.