Personality Trait Classification of Essays with the Application of Feature Reduction

Determining an individual’s personality traits is an important concept in Psychology. Although traits are normally assessed through self-report tests, an alternative method would be to computationally analyze an individual’s linguistic markers. Studies in personality trait classification show promising results and look to continuously improve the field by either using new features or by collecting new data from social media; however, a key concept that is not always considered is the use of feature reduction techniques. This research aims to perform feature reduction techniques on linguistic features from essays and classify the author’s personality traits based on the reduced feature set. The classifiers are evaluated by comparing against a baseline classifier trained with all extracted features. The feature reduction techniques used are Information Gain and Principal Component Analysis. The results show that feature reduction techniques are able to increase classification measures, but not by significant values. Reduced datasets are exceptionally beneficial in reducing the amount of data needed allowing classifiers to perform faster while still maintaining classification measures.