Predicting User Personality from Public Perceptions on Social Media
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Personality distinctively characterises an individual and profoundly influences behaviours. Social media offer the virtual community an unprecedented opportunity to generate content and share aspects of their life which often reflect their personalities. The interest in using deep learning to infer traits from digital footprints has grown recently; however, very limited work has been presented which explores the sentiment information conveyed. The present study, therefore, used a computational approach to classify personality from social media by gauging public perceptions underlying factors encompassing traits. In the research reported in this thesis, a Sentiment-based Personality Detection system was developed to infer trait from short texts based on the ’Big Five’ personality dimensions. We exploited the spirit of Neural Network Language Model (NNLM) by using a unified model that combines a Recurrent Neural Network named Long Short-Term Memory (LSTM) with a Convolutional Neural Network (CNN). The proposed system is threefold: It commences with sentiment classification by grouping short messages harvested online into three categories, namely positive, negative, and nonpartisan. This is followed by employing Global Vectors (GloVe) to build vectorial word representations. As such, this step aims to add external knowledge to short texts. We apply CNN and LSTM during the learning process. Finally, we trained each variant of the models to compute prediction scores across the five traits. Experimental study indicated the effectiveness of our system. As part of our investigation, a case study was carried out which employed the proposed system. We opted for Uber, a renowned global hail-sharing company, as the subject of our examination. The selected study was set up to investigate the existing correlation of personality traits and opinion polarities. The results support the prior findings of the tendency of persons with the same traits to express sentiments in similar ways.