Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

• URL: http://arxiv.org/abs/2007.04571v3
• Date: Wed, 8 Jun 2022 09:11:31 GMT
• Title: Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
• Authors: Majid Ramezani, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, Meysam Asgari-Chenaghlu, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi, Mehrdad Ranjbar-Khadivi, Zoleikha Jahanbakhsh-Nagadeh, Elnaz Zafarani-Moattar, Taymaz Rahkar-Farshi
• Abstract summary: The major objective of this study is to enhance the accuracy of Automatic Personality Prediction from the text. We suggest five new APP methods including term frequency vector-based, ontology-based, enriched semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. The results show that ensemble modeling enhances the accuracy of APP.
• Score: 9.175926602405543
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

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