Random Multimodal Convolutional Forward Taylor Network for Personality Prediction using MBTI Data
DOI:
https://doi.org/10.29304/jqcsm.2024.16.41785Keywords:
Personality Prediction, Random Multimodal Deep Learning, Convolutional Neural Network Taylor Series;Abstract
The growth of social media for self-awareness and self-expression has raised more attention to the Myers-Briggs Type Indicator (MBTI) for analyzing people's personalities. Nevertheless, additional research is required to determine various word embedding and data handling methods to enhance the accuracy of MBTI personality-type predictions. Therefore, a new technique known as Random Multimodal Convolutional Forward Taylor Network (RMConv FT-Net) is devised to predict personality. Initially, text data from the database is considered input, and then tokenization is performed to split the text into tokens, which is done by Bidirectional Encoder Representations from Transformers (BERT). After this, various features, including linguistic features and several other features, like Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and so on, are mined from the text. Following this, the data imbalance problem is addressed by employing oversampling. Finally, personality prediction is performed by utilizing RMConv FT-Net, designed with the incorporation of the Convolutional Neural Network (CNN), Taylor series, and Random Multimodal Deep Learning (RMDL). The experimental outcomes of RMConv FT-Net based on Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) show that it obtained the values of 0.017, 0.047, and 0.129.
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