Survey Analysis Framework for Systematic Analysis Using AI and GEN AI Tools Paper

When organizations collect survey data from their stakeholders and employees to gauge their sentiments, opinions, and experiences, the analysis of mixed-format survey data, which combines structured multiple-choice questions (MCQs) and unstructured open-ended responses, poses a significant challenge in extracting scalable, actionable insights that can inform business decisions. However, traditional survey analysis methods are plagued by limitations, including the requirement for extensive manual effort, domain expertise, and disjointed workflows, which hinder the efficient extraction of meaningful insights from mixed-format data. The primary challenge lies in deriving actionable insights from mixed-format survey data, which demands a unified, AI-driven approach that can automate sentiment analysis, identify outliers, and uncover hidden patterns in survey responses. To address this challenge, this study proposes a unified, AI-driven framework that integrates classical machine learning (ML) with Generative AI (GenAI) to provide a comprehensive understanding of overall survey results including sentiments, outliers and other analytics. The synergy between machine learning and GenAI enables the framework to accurately identify patterns and trends, achieving a predictive accuracy and yielding insightful results comparable to human annotation. An evaluation of the framework's effectiveness, based on a stakeholder satisfaction survey, reveals high satisfaction rates with respect to free-text summarization quality (89%) and theme extraction efficiency (90%), demonstrating the potential of this hybrid approach to revolutionize survey research methodologies and inform evidence-based decision-making across domains such as healthcare, education, and enterprise analytics.