Naïve Bayes-Support Vector Machine Combined BERT to Classified Big
Five Personality on Twitter

Naïve Bayes-Support Vector Machine Combined BERT to Classified Big
Five Personality on Twitter

BERT, Big Five Personality, LIWC, Naïve Bayes-Support Vector Machine

Twitter is one of the most popular social media used to interact online. Through Twitter, a person's personality can be
determined based on that person's thoughts, feelings, and behavior patterns. A person has five main personalities likes
Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. This study will make five personality predictions
using the Naïve Bayes method – Support Vector Machine, Synthetic Minority Over Sampling Technique (SMOTE), Linguistic
Inquiry Word Count (LIWC), and Bidirectional Encoder from Transformers Representations (BERT). A questionnaire was
distributed to people who used Twitter to collect and become a dataset in this research. The dataset obtained will be processed
into SMOTE to balance the data. Linguistic Inquiry Word Count is used as a linguistic feature and BERT will be used as a
semantic approach. The Naïve Bayes method is used to perform the weighting and the Support Vector Machine is used to
classify Big Five Personalities. To help improve accuracy, the Optuna Hyperparameter Tuning method will be added to the
Naïve Bayes Support Vector Machine model. This study has an accuracy of 87.82% from the results of combining SMOTE,
BERT, LIWC, and Tuning where the accuracy increases from the baseline

Billy Anthony Christian Martani1
, Erwin Budi Setiawan2

Telkom University

30-04-2022

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Indonesia

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