A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks

Taghvaei, N.; Masoumi, B.; Keyvanpour, M. R.

doi:10.22044/jadm.2021.10583.2197

Document Type : Original/Review Paper

Authors

¹ Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

² Department of Computer Engineering, Alzahra University, Vanak, Tehran, Iran.

https://doi.org/10.22044/jadm.2021.10583.2197

Abstract

In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.

Keywords

20.1001.1.23225211.2021.9.3.2.7

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A Hybrid Framework for Personality Prediction based on Fuzzy Neural Networks and Deep Neural Networks

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References

Volume 9, Issue 3July 2021Pages 283-294

Volume 9, Issue 3
July 2021
Pages 283-294