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.

翻译：由于信息基础设施(特别是互联网和社交媒体)的传播,人类通信在面对面的通信方面进行了显著改革。一般而言,自动个性预测(或感知)(APP)是对不同类型人类生成/交换的内容(如文字、语言、图像、视频等)的个性进行自动预测。本研究的主要目标是提高文本中的AP的准确性。为此,我们建议五种新的AP方法,包括基于频度的、基于本科学的、丰富的基于本科学的、基于潜藏的语义分析(LSA)和基于深层学习的BILSTM(BILSTM)方法。这些方法作为基础,通过以元模型的分级关注网络(HAN)的混合建模(刷),有助于提高AP的准确性。结果显示,组合建模提高了AP的准确性。