AI Knows You: Deep Learning Model for Prediction of Extroversion Personality Trait - Anam Naz; Hikmat Ullah Khan; Sami Alesawi; Omar Ibrahim Abouola; Ali Daud; Muhammad Ramzan (IEEE Access)
The recent rise of Artificial Intelligence (AI) has already revolutionized human lives and is improving the quality of human life in many ways. The field of AI, Natural Language Processing (NLP), helps to understand, comprehend and even generate new content. NLP is used for various content analysis tasks such as sentiment analysis, fake news detection, etc. However, human personality traits detection is a new research domain. Analyzing users generated content has a significant role in identifying and understanding users’ views and behaviors. Users’ traits detection can be helpful in analysis of consumers’ personalization, finding top candidates for recruitment, career counselling, etc. In this research study, our aim is to predict personality of extroversion behaviors using machine and deep learning approaches. Extroversion means whether a person is an introvert or extrovert as this trait is relevant to certain jobs like team management, social ties etc. For empirical analysis, we investigate MBTI dataset with various feature engineering techniques including textual features like Term Frequency-Inverse Document Frequency (TF-IDF), Parts of Speech (PoS) tagging, as well as deep word embeddings ok word2vec, GloVe. The state-of-the-art shallow machine learning, ensemble modelling and deep learning models are applied. The main novelty is the exploration of latest sentence embeddings which captures semantic information of content in a better manner. Thus, the comprehensive results analysis reveals sentence embeddings as features to Bi-LSTM achieves highest accuracy of 92.52% and outperforms the existing studies in the relevant literature.