The contemporary professional land- scape presents a multifaceted challenge for individuals seeking to align their inherent cognitive traits with viable career paths. Traditional psychometric evaluations and aptitude tests often offer static, one-dimensional snapshots of a candidate's potential, failing to capture the nuanced evolution of emotional intelli- gence, behavioural patterns, and intrinsic motivations. MindPrint addresses this gap by introducing an innovative Human- Computer Interaction (HCI) framework that utilizes Natural Language Process- ing (NLP) to perform real-time sentiment analysis and personality trait extraction from user-written journal entries. By leveraging the Hugging Face Inference API and transformer-based architectures, the system transforms unstructured tex- tual data into a structured cognitive pro- file. This paper details the design, im- plementation, and rigorous evaluation of MindPrint. We discuss the integration of React.js, Node.js, and MongoDB in creat- ing a scalable architecture capable of gen- erating personalized career recommen- dations with high accuracy. Experimental results indicate that MindPrint achieves 94.5% accuracy in sentiment detection and significantly outperforms traditional assessment models in longitudinal trait tracking.
Pawar et al. (Thu,) studied this question.