Abstract:
The Myers-Briggs Type Indicator (MBTI) is a popular personality classification tool that utilizes the work of Carl Jung. With individuals increasingly expressing themselves online rather than in person, social media has become a promising platform for predicting personality. However, predicting personality from online behavior is a challenging task that requires extensive data processing and modeling. To tackle this challenge, a novel approach is proposed that leverages a transformer-based summarization model to summarize the dataset records before applying the DistilBERT base classification model. The proposed method improves the MBTI classification task by achieving an accuracy rate of 0.96, which demonstrates the efficacy and durability of the strategy. The study emphasizes the importance of transformer-based summarization in enhancing NLP tasks and the need for applying various optimization techniques to achieve optimal performance. The findings provide a foundation for future research in personality classification and NLP.
