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Personality Traits Classification on Twitter

Personality traits have been shown to have strong influences on important aspects of life such as success in the workplace, political temperament, and general emotional stability. Computer-based personality assessments using information from social networking platforms have shown to be more accurate than judgments made by people close to the subject. This paper presents a personality traits classification system that incorporates language-based features, based on count-based vectorization (TF-IDF) and the GloVe word embedding technique, with an ensemble prediction system consisting of gradient-boosted decision trees and an SVM classifier. This combination allows to reliably estimate certain personality traits using only the latest 50 tweets from a user’s profile. The performance of the proposed system is validated on a large, publicly available dataset and compares favourably with other state-of-the-art methods.

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