摘要：AbstractPersonality Traits Detection is one of the important problems as a text analytics task in Natural Language Processing (NLP). Text analytics is the process of finding out insight knowledge over written text. Although most deep learning models give high performance, they often lack interpretability. Computer Vision (CV) has been affected significantly with inductive transfer learning, however training from scratch and task-specific modifications are still wanted in many NLP techniques.This paper addresses the problem of personality traits classification. We adopted the use of the Universal Language Model Fine-Tuning (ULMFiT) in personality traits detection. The model makes use of transfer learning rather than the classical shallow methods of word embedding and proved to be the most powerful model in many NLP problems.The basic advantage of using this model is that there is no need to do feature engineering before classification. When applied to benchmark dataset, the proposed method shows a statistical accuracy improvement of about 1% compared to the state-of-the-art results for the big five personality traits.
关键词：Big Five Personality Model;Personality Traits;LSTM;NLP;Text Analytics;Deep Learning;ULMFiT
