Automated personality trait detection from text data has emerged and gained a great deal of attention in the subject area of affective computing and sentiment analysis. Most previous work has focused on features engineering such as linguistic styles and psycholinguistic databases which have correlations with personality. Recently, natural language processing has been affected significantly with transfer learning based on feature extraction and fine-tuning pre-trained language models. We propose a new deep learning-based model for personality prediction and classification using both data and classifier level fusion. The model gets benefit from, transfer learning in natural language processing through leading pre-trained language models namely Elmo, ULMFiT, and BERT. The proposed model demonstrates the powerfulness of the introduced method to be a promising personality prediction model. When evaluating the proposed method, results show a competitive and significant accuracy enhancement of about 1.25% and 3.12% in comparison to the most recent results for the two gold standard Essays and myPersonality datasets for personality detection.

基于文本数据的自动人格特征检测已经出现，并在情感计算和情感分析的主题领域获得了极大的关注。以前的大多数工作都集中在特征工程上，例如与个性相关的语言风格和心理语言数据库。最近，基于特征提取和微调预训练语言模型的迁移学习对自然语言处理产生了重大影响。我们提出了一种新的基于深度学习的模型，用于使用数据和分类器级别的融合进行个性预测和分类。该模型通过领先的预训练语言模型（即 Elmo、ULMFiT 和 BERT）受益于自然语言处理中的迁移学习。所提出的模型证明了所引入的方法作为一种有前途的人格预测模型的强大功能。在评估所提出的方法时，结果显示，与用于个性检测的两个黄金标准 Essays 和 myPersonality 数据集的最新结果相比，准确度提高了约 1.25% 和 3.12%。