Personality Prediction Based on Text Analytics Using Bidirectional Encoder Representations from Transformers from English Twitter Dataset

Abstract

Personality traits can be inferred from a person’s behavioral patterns. One example is when writing posts on social media. Extracting information about individual personalities can yield enormous benefits for various applications such as recommendation systems, marketing, or hiring employees. The objective of this research is to build a personality prediction system that uses English texts from Twitter as a dataset to predict personality traits. This research uses the Big Five personality traits theory to analyze personality traits, which consist of openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several classifiers were used in this research, such as support vector machine, convolutional neural network, and variants of bidirectional encoder representations from transformers (BERT). To improve the performance, we implemented several feature extraction techniques, such as N-gram, linguistic inquiry and word count (LIWC), word embedding, and data augmentation. The best results were obtained by fine-tuning the BERT model and using it as the main classifier of the personality prediction system. We conclude that the BERT performance could be improved by using individual tweets instead of concatenated ones.