Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

TL;DR

This paper proposes five new methods for automatic personality prediction from text and combines them using ensemble stacking with a hierarchical attention network to improve accuracy.

Contribution

Introduction of five novel APP methods and their integration through ensemble stacking with HAN to enhance prediction accuracy.

Findings

Ensemble stacking improves APP accuracy.

Deep learning-based method (BiLSTM) contributes significantly.

Hierarchical attention network effectively combines base models.

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.