Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
This work addresses the challenge of predicting human personality from text, which is relevant for applications in psychology and social media analysis, but it is incremental as it builds on existing APP methods.
The study tackled the problem of improving the accuracy of Automatic Personality Prediction (APP) from text by proposing five new methods and using ensemble modeling with a hierarchical attention network as the meta-model, resulting in enhanced accuracy.
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.