Study on Predicting Psychological Traits of Online Text by BERT

With the rapid development and popularity of the Internet, an increasing number of people would like to use online platforms to express themselves and communicate with others. It is inevitable that a large number of online text data are constantly emerging with personal information, which often indicate individual real expression in different conditions and reflect personal inner psychological traits and personality tendency. Applying text mining techniques to analyzing psychological traits behind the online text is not only helpful for individuals to understand themselves, but also useful to avoid the motivation interfere while using traditional methods for psychological assessment. In recent years, the language model named bidirectional encoder representations from transformers (BERT) has greatly improved the performance of both the text classification task and the sentiment analysis task. In this paper, prediction models for psychological traits are constructed based on online text. Comprehensive semantic features and long dependency in the context are obtained by BERT. Considering that distinct algorithm frameworks of classifiers can lead to different classification results, the fully-connected layer of the BERT_BASE model and the random forest algorithm are used in the downstream classification task to make comparison. The results show that psychological traits can be effectively predicted from text classification based on BERT, and the average accuracy, average precision and other indicators are more than 97%.