Personality Classification Based on Textual Data using Indonesian Pre-Trained Language Model and Ensemble Majority Voting

Abstract

Personality is a collection of striking traits and behaviors of a person. The use of personality models can be applied in employee recruitment systems or to analyze characteristics and potential in more depth. Personality models are usually made using psychological test data or filling out questionnaires. However, this requires a long time. Building a personality classification model using NLP and deep learning is considered one of the best solutions. However, the performance of the classification model still needs to be improved, especially for Indonesian Language data. So, this research makes a personality classification model with Indonesian Language data using BERT-based architectures such as Multilingual BERT, IndoBERT, and Indonesian RoBERTa Base with an ensemble majority voting technique. Data limitations and imbalances were addressed using synonym replacement by incorporating words from a pre-trained model, MBERT. Information contained in social media often has ambiguous meanings because the words conveyed are not standardized, so this study tries to retain the information contained in the text by translating emoticons and slang words at the preprocessing stage to help keep the meaning of words in context. The proposed approach's research results can improve the classification model's results in classifying personality.