A Semantic Analysis Approach to Extract Personality Traits from Tweets (X)

Abstract

The utilization of social networks has experienced a substantial surge in the past decade, with individuals routinely exchanging and consuming personal data. This data, subject to analysis and utilization across diverse contexts, has spurred scholarly interest in discerning the personality traits of social network users. Personality, as an intrinsic characteristic, distinguishes individuals in terms of cognition, emotion, and behavior, thereby influencing social relationships and interactions. Among the extensively studied frameworks elucidating personality variance is the Five Factor Model, commonly referred to as the "Big Five," encompassing Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism (OCEAN). Personality assessment holds practical utility across domains such as education, security, marketing, e-learning, healthcare, and personnel management. Prior investigations have demonstrated the feasibility of automatic text analysis in personality discernment. This paper introduces a multi-agent methodology grounded in semantic similarity metrics for personality trait recognition via automatic text analysis of Tweets. Our approach leverages WordNet and information content-based semantic similarity measures to analyze tweet content and classify users' personality traits. Experimental results demonstrate the effectiveness of our method, achieving a remarkable 96.28% accuracy in identifying personality traits from Tweets. This high success rate underscores the potential of our semantic analysis approach in accurately profiling social media users' personalities, offering valuable insights for various applications in behavioral analysis and personalized services.