Researchers have introduced a novel system to analyze the semantics of an individual's tweets using Receptiviti's LIWC and SALLEE emotion frameworks to evaluate candidates for various applications. With increased access to phones and the internet, many organizations are focusing on making credit systems available to the masses by introducing psychometric analysis. The proposed dynamic model allows stakeholders to ascertain the personality of a Twitter user according to any personality model, and the linguistic score is heavily correlated with certain personality types.
The study found that the proposed approach outperformed other relevant works and provides a viable and flexible approach to model any kind of personality model. This research highlights the potential for using social media data for psychometric analysis, particularly in the wake of the pandemic's shift to virtual operations for many businesses. The findings suggest that Twitter profiles can provide valuable insights into an individual's personality and that this approach could have significant implications for a range of applications, from credit analysis to recruitment.
