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Personality Prediction of Social Network Users Using Ensemble and XGBoost
Published in Springer
Volume: 1119
Pages: 133 - 140
Machine learning has gained tremendous attention from researchers recently. It has wide applications in tasks such as prediction and classification. Current work focuses on the effective detection of the personality of social network users. Personality is a combination of one’s thinking and behavior. Having knowledge about personality of a person has many applications in real world such as varied recommendation systems or HR departments. Personality of a person can be better understood by interacting with him/her. Predicting personality using social media is a new approach where direct interaction with people can be eliminated and accurate predictions can be built. Although different machine learning methods have been used by researchers recently for the task of prediction, the use of Ensembles has not been explored. Current work focuses on advanced classifiers such as XGBoost and Ensemble for prediction. Experimentation on the real-time Twitter dataset indicates high accuracy of 82.59% with an Ensemble. These results are encouraging for future research. © 2020, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer