Personality Detection from Text using Convolutional Neural Network

The distinguishing characteristics belong to a person are personality traits, which is predicted from person's behavioral pattern. As most of the people provide lots of information knowingly or unknowingly into their writings, it is possible to extract personality traits from those texts. Individual personality traits detection from texts, yields enormous possibilities toward various applications named as forensic department, mental health diagnosis, etc. Meanwhile, deep learning algorithm performs fairly well in text based personality detection; however, its performance may vary with activation functions. Hence, this paper proposed an empirical approach to find the best personality detection performance by comparing several activation functions named as sigmoid, tanh, and leaky ReLU. Here, text documents were pre-processed and vectorized for input in convolutional neural network. The input size was multiple to length of word, sentence, documents, and feature vectors. Five personality traits named as EXT, NEU, AGR, CON, and OPN have been used for experimental analysis. The result showed that tanh and leaky ReLU performs over sigmoid in all datasets. The average F1-score of sigmoid, tanh and leaky ReLU showed 33.11%, 47.25%, and 49.07% respectively. However, Fl-score of leaky ReLU was high only for CON, tanh showed better result for others datasets. The overall performance showed by tanh is better than sigmoid and leaky ReLU for personality detection from text.