论文标题
自动人格预测；使用集合建模的增强方法
Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling
论文作者
论文摘要
他/她在讲话或写作中使用的那些词表现得很重要。由于传播信息基础架构（特别是互联网和社交媒体），人类通讯从面对面的交流进行了显着改革。通常，自动人格预测（或感知）（APP）是对不同类型的人类生成/交换内容（例如文本，语音，图像，视频等）对个性的自动预测。这项研究的主要目的是从文本中提高应用程序的准确性。为此，我们建议使用五种新的应用程序方法，包括基于术语频率向量的基于本体的，基于本体的基于本体的潜在语义分析（LSA）基于基于本体的频率，基于本体的频率，基于本体，基于本体，基于本体频率，以及基于深度学习的（BILSTM）方法。这些方法是基本方法，可以通过基于层次注意网络（HAN）作为元模型的集合建模（堆叠）来提高应用程序的准确性。结果表明，整体建模增强了应用的准确性。
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.
