Personality Prediction of the Users Based on Tweets through Machine Learning Techniques

Abstract

With the advancement of interpersonal organizations, the massive rich data from Social platforms such as Facebook, YouTube, Instagram, and Twitter supply determining information about Social communications and human manners. An assortment of approaches has been created to characterize clients' characters dependent on their social exercises and language use propensities. Specific methodologies vary to various AI calculations, information sources, and capabilities. The Informal community application records the enormous measure of users' conduct communicated in different exercises like preferences, notices, posts, remarks, photographs, labels, historical textual features, tweets, user profiles, and offers. Different analysts use numerous old Machine Learning calculations in establishing their models. This examination attempts to execute a few extreme learning designs to see the correlation by exhaustive investigation strategy through the exact results. We inspect the presence of plans of interpersonal organizations and semantic attributes comparative with character connections utilizing the myPersonality project dataset. The investigation also looks at two AI models and plays out the link between every one of the feature sets and personality traits. The outcomes for the forecast exactness show that regardless of whether tried under a similar dataset, the character expectation framework based on the Logistic Regression classifier outflanks the standard for all the included sets, prediction accuracy of 98.9%. The best prediction accuracy of 99.8% is gained by using the Random Forest classifier.