Fusion Methods for Forecasting Personality Employing Machine Learning
Keywords:
Personality, Support Vector Machine Stacker (SVMS), ASCII Formats, BERT and ULMFiT, Machine learning (ML)Abstract
Personality prediction has gained substantial attention in various fields, such as psychology and human-computer interaction. Traditional approaches to personality prediction often rely on self-report questionnaires, which can be time-consuming, subject to biases, and limited by the respondent's self-awareness. In this paper, we proposed a Support Vector Machine Stacker (SVMS) for the categorization and prediction of personalities, leveraging the fusing of data and classification levels. Our model uses Learning to transfer in natural language processing by utilizing two the BERT annulment, one of the top pre-trained language models. By incorporating these powerful language models, our proposed approach demonstrates promising results in personality prediction. In our experiments, we compare the SVMS against other methods to evaluate the performance of the proposed model. The evaluation metrics include accuracy, precision, recall, and F1-score. The results demonstrate that the SVMSachieves competitive performance in predicting personality traits.
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Copyright (c) 2024 Karishma Desai, Girish Kalele, Rakesh Kumar Yadav, Bhuvana Jayabalan, Manish Kumar Goyal
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