 Text Mining and Classification Prediction Based on Twitter MBTIPeishan Li GBP Proceedings Series, 2025, vol. 16, 87-101 Abstract: The MBTI personality test has gained substantial attention in recent years and has become a prominent topic in contemporary society. Numerous individuals and institutions employ this psychometric tool to inform and guide important decisions, ranging from personal development to organizational management. This study applies advanced text mining techniques to analyze Twitter data, aiming to investigate whether there are significant differences among MBTI types in terms of posting frequency, post length, and sentiment expression. In addition, machine learning algorithms are utilized to attempt the prediction of a user's personality type based solely on the textual content they produce. The study further evaluates the effectiveness of the MBTI framework in predicting online language styles and behavioral patterns. Empirical results indicate that analyzing user-generated content can achieve a certain level of accuracy in predicting personality traits, demonstrating practical applicability in digital behavioral studies. This approach provides meaningful insights for the field of text data mining, offering a foundation for leveraging machine learning methods to gain a deeper understanding of online user behaviors and to enhance personalized digital interactions. Keywords: text mining; MBTI; sentiment analysis; classification prediction (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:axf:gbppsa:v:16:y:2025:i::p:87-101 Access Statistics for this article More articles in GBP Proceedings Series from Scientific Open Access Publishing |
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