Big Data Analysis of Facebook Users Personality Reconition using Map Reduce Back Propagation Neural Networks

Abstract

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance. \xa0Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.