Who Am I? Personality Detection Based on Deep Learning for Texts

Recently, personality detection based on texts from online social networks has attracted more and more attentions. However, most related models are based on letter, word or phrase, which is not sufficient to get good results. In this paper, we present our preliminary but interesting and useful research results to show that the structure of texts can be also an important feature in the study of personality detection from texts. We propose a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts. Besides, a concept, Latent Sentence Group (LSG), is put forward to express the abstract feature combination based on closely connected sentences and we use our model to capture it. To the best of our knowledge, most related works only conducted their experiments on one data set, which may not well explain the versatility of their models. We implement our evaluations on two different kinds of datasets, containing long texts and short texts. Evaluations on both datasets have achieved better results, which demonstrate that our model can efficiently learn valid text structure features to accomplish the task.