Multi-label personality trait identification from text

Understanding the personality is beneficial for many purposes, e.g., it is natural to predict a user’s personality before offering him or her any services. The personality is intrinsic in the behavior of a person in all aspects, such as text writing. Some work has been proposed in recent times for correctly classifying a person’s personality from the text. However, it is still a significant challenge as the achieved accuracy is low; therefore, the proposed work addresses this issue. Effective feature selection techniques provide better classification accuracy in multi-label classification and personality traits identification as multi-label classification problem requires efficacy of feature selection methods. Therefore, to improve the accuracy using feature selection technique, this paper proposes a method for personality trait recognition from textual data called P ersonality T rait Classification based on L inguistic and F eature selection as M ulti-label classification (PTLFM). It combines analysis of variance’s F-statistic, Chi-square, and Mutual information with the sequential feature selection wrapper method to rank features. These three criteria apprehend different aspects of the dataset. The experimental results demonstrate that the proposed PTLFM method achieves higher accuracy across all the personality traits than the prevailing state-of-the-art machine learning and deep learning models. PTLFM provides an impressive absolute improvement of 2.23% and 3.84% of comparative improvement over the existing prevalent method, with more than 90% of features discarded. Furthemore, the proposed PTLFM achieves a percentage gain compared to the competitive methods across different personality traits Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness in absolute terms 1.17, 1.94, 2.35, 1.64, and 0.35 respectively, and in comparative terms 2.01, 3.27, 4.14, 2.86, and 0.56 respectively. The results suggest that although deep learning is a popular paradigm, it does not always lead to a better predictive performance than machine learning models in all the problem domains.