Using Machine Learning Based Models for Personality Recognition
This work addresses automatic personality recognition for applications in cognitive science, but it is incremental as it builds on existing CNN approaches by adding AdaBoost for ensemble learning.
The paper tackled personality recognition from text by proposing a deep learning method combining Convolutional Neural Networks (CNN) with AdaBoost, and demonstrated its superiority over existing machine learning and deep learning methods on the Essay dataset.
Personality can be defined as the combination of behavior, emotion, motivation, and thoughts that aim at describing various aspects of human behavior based on a few stable and measurable characteristics. Considering the fact that our personality has a remarkable influence in our daily life, automatic recognition of a person's personality attributes can provide many essential practical applications in various aspects of cognitive science. deep learning based method for the task of personality recognition from text is proposed in this paper. Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. Our proposed method was validated on the Essay dataset by conducting a series of experiments and the empirical results demonstrated the superiority of our proposed method compared to both machine learning and deep learning methods for the task of personality recognition.