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.

PDF Abstract
No code implementations yet. Submit your code now

Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Personality Trait Recognition Essays Ensemble Modeling Precision 60.48 # 1
Recall 61.66 # 2
F-Measure 61.04 # 2
Accuracy 60.24 # 1
Personality Trait Recognition Essays Ontology-Based Precision 51.42 # 2
Recall 100 # 1
F-Measure 67.91 # 1
Accuracy 51.42 # 2

Methods


No methods listed for this paper. Add relevant methods here