Sentiment Dynamics of Graduating Students-to-Employees Transitions: A Paired Sample LLM-Based Analysis of Expectations and Real-World Experiences

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Abstract
The transition from student life to professional employment represents a challenging shift marked by emotional and practical adjustments. This research explores the sentiment dynamics of two surveys conducted with two different groups from the Science and Technology background: Fresh graduates who joined the workplace and students who are in their final phase of academic life and likely to start their professional life. Exploratory data analysis was performed to process numerical and categorical responses, while an autonomous thematic analysis was introduced after implementing similarity-based clustering using K-Means in open-ended responses. Subsequently, Google Pegasus-Large has been employed to summarize open responses and assign preliminary clustering to the responses, and the GPT-5.1 model has been used to extract themes and codes with sentiment labels, which were later reviewed through manual validation. By capturing and analyzing these sentiments, the research identifies patterns in expectation management, resilience building, and coping mechanisms for workplace stress. In this article, we have provided guidance for targeted employee support and organizational adaptation, supporting human factors–informed interactive tools to optimize user experiences, easing the academic-to-work transition for newprofessionals. Finally, this research provides a foundation for improving employee well-being, effective organizational practices, and healthier work environments.
Funding Information
  • College of Infromation Science, King Saud University (ORF-2026-18)
  • United International University (IAR-2025-Pub-070)

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