DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA

Determining the personality dimensions of the individuals is very important in the psychological research works. The most well-known example of personality dimensions is the five-factor model (FFM). There are two approaches, manual and automatic, for determining the personality dimensions. In a manual approach, the Psychologists discover these dimensions through the personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this work, we propose a method called DENOVA (DEep learning based on the aNOVA), which predicts FFM using deep learning based on an Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA in order to select the most informative terms. Then DENOVA employs Word2Vec in order to extract document embeddings. Finally, DENOVA uses support vector machine (SVM), logistic regression, XGBoost, and multi-layer perceptron (MLP), as classifiers in order to predict FFM. The experimental results obtained show that DENOVA outperforms on average, 6.91%, the state-of-the-art methods in predicting FFM with respect to accuracy.