Predicting Personality Traits and Social Context Based on Mining the Smartphones SMS Data

Citation:
Fatma Yakoub, Moustafa Zein, K. Y. A. A. A. E. H., "Predicting Personality Traits and Social Context Based on Mining the Smartphones SMS Data", Proceedings of the Second Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2015, , Ostrava, Czech Republic, , June 29 - July , 2015.

Date Presented:

June 29 - July

Abstract:

Reality Mining is one of the first efforts that have been exerted to utilize smartphone’s data; to analyze human behavior. The smartphone data are used to identify human behavior and discover more attributes about smartphone users, such as their personality traits and their relationship status. Text messages and SMS logs are two of the main data resources from the smartphones. In this paper, The proposed system define the user personality by observing behavioral characteristics derived from smartphone logs and the language used in text messages. Hence, The supervised machine learning methods (K-nearest nighbor (KNN), support vector machine, and Naive Bayes) and text mining techniques are used in studying the textual matter messages. From this study, The correlation between text messages and predicate users personality traits is broken down. The results provided an overview on how text messages and smartphone logs represent the user behavior; as they chew over the user personality traits with accuracy up to 70 %.

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