Leveraging NLP and Machine Learning for Mapping Digital Footprints to Personality

Abstract

Personality prediction from social media data has gained substantial attention due to its applications in fields like marketing, psychology, and personalized services. This paper explores the prediction of Myers-Briggs Type Indicator  (MBTI)  personality  types  from  so- cial media content using  machine  learning  techniques. By  analyzing linguistic patterns and behavioral indicators from user posts, the proposed model aims to map these characteristics to the MBTI’s 16 personality types. The study leverages natural language processing (NLP) methods, including tokenization, sentiment analysis, and n- gram extraction, to create feature-rich representations of the data. Multiple machine learning algorithms, such as Support Vector Ma- chines (SVM), decision trees, and ensemble methods,  are employed to evaluate the model’s accuracy. The results demonstrate that the ensemble model, which integrates Logistic Regression, Random For- est, and Gradient Boosting, achieves superior performance compared to individual classifiers.  This research highlights the potential of using digital footprints for personality prediction and presents a robust approach that significantly improves classification accuracy, precision, recall, and F1 scores over existing methods.