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- Title
Detection of Personality Features From Handwriting By Machine Learning Methods.
- Authors
Müsevitoğlu, Hilal; Öztürk, Ali; Başünal, Fatiha Nur
- Abstract
Handwriting contains a lot of information about the person writing it and is a sign of personality traits represented by neurological patterns in the brain. In other words, our brain and subconscious actually shape our character as a result of our habits. It is possible to get an idea about the mood of the individual by examining the handwriting. Joy, sadness, anger and anxiety are some of them. In this study, handwritings of people belonging to different professions and age groups were collected. Feature extraction methods was applied on these articles by applying word and line detection, slant, pressure, page layout and similar image processing methods. The obtained features formed the inputs of the dataset. Personality traits such as calm, optimistic, emotional, extrovert, which were estimated using graphology, were added to the dataset as outputs. Then, this dataset was applied to Random Forest (RF), Naive Bayes (NB), Decision Tree, Support Vector Machines (SVM), Logistic Regression, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms. According to the performance metrics used, the Random Forest algorithm gave the most successful results in terms of accuracy, precision and f1-score metrics. For this algorithm, the accuracy, precision, recall and f1 score values were found to be 0.90, 0.91, 0.84 and 0.85, respectively. Furthermore, the results of the personality analysis were compared with the results of the personality test performed by the expert psychologist. As a result of this comparison, it was seen that there was a 73% match.
- Subjects
HANDWRITING; LOGISTIC regression analysis; RANDOM forest algorithms; DECISION trees; LEARNING classifier systems
- Publication
Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi, 2023, Vol 9, Issue 2, p200
- ISSN
2149-4916
- Publication type
Article
- DOI
10.30855/gmbd.0705064