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Journal of Artificial Intelligence and Data Mining، جلد ۹، شماره ۴، صفحات ۴۵۱-۴۶۳
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عنوان انگلیسی DENOVA: Predicting Five-Factor Model using Deep Learning based on ANOVA
چکیده انگلیسی مقاله Determining the personality dimensions of individuals is very important in psychological research. The most well-known example of personality dimensions is the Five-Factor Model (FFM). There are two approaches 1- Manual and 2- Automatic for determining the personality dimensions. In a manual approach, Psychologists discover these dimensions through personality questionnaires. As an automatic way, varied personal input types (textual/image/video) of people are gathered and analyzed for this purpose. In this paper, we proposed a method called DENOVA (DEep learning based on the ANOVA), which predicts FFM using deep learning based on the Analysis of variance (ANOVA) of words. For this purpose, DENOVA first applies ANOVA to select the most informative terms. Then, DENOVA employs Word2Vec to extract document embeddings. Finally, DENOVA uses Support Vector Machine (SVM), Logistic Regression, XGBoost, and Multilayer perceptron (MLP) as classifiers to predict FFM. The experimental results show that DENOVA outperforms on average, 6.91%, the state-of-the-art methods in predicting FFM with respect to accuracy.
کلیدواژه‌های انگلیسی مقاله FiveFactor Model (FFM), ANOVA, deep learning, word embedding, Text mining
نویسندگان مقاله M. Nasiri |
School of Computer engineering, Iran University of Science and Technology, Tehran, Iran.

H. Rahmani |
School of Computer engineering, Iran University of Science and Technology, Tehran, Iran.

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_2141_002771c5c1b19fc3f9996a035c4f75f4.pdf
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