Assessing Emotional Intelligence from Textual Cues:A Natural Language Processing Approach to Emotional Quotient Prediction

AbstractRecent years have seen increased interest in applying AI methods to text data for EI andEQ assessment. Emotional intelligence (EI) and emotional quotient (EQ)are two key aspectsof human thought and behavior that have an impact on decision-making, social interaction,and well-being. This paper aims to investigate the possibilities that comment analysis canoffer in the automated discernment of emotional intelligence and quotient from textual data.By analyzing Twitter data, this paper tries to find out how exactly language is connected toEI, proposing a model to predict EQ scores based on textual data. The study implements anemotion classifier based on transformers, extracts emotion scores from each text sample, andmaps these values to the five components of EI - self-awareness, self-regulation, motivation,empathy, and social skills. In the second part, the derivation of numerical EQ scores andqualitative feedback in the rule-based translator is automatically carried out with emotionprofiles. We employ a pipeline for emotion identification or recognition that is mapped to fiveEI pillars: self-awareness, self-regulation, motivation, empathy, and social skills.