Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis

Text data augmentation is a widely used strategy for mitigating data sparsity in natural language processing (NLP), particularly in low-resource settings where limited samples hinder effective semantic modeling. While augmentation can improve input diversity and downstream interpretability, existing techniques often lack mechanisms to ensure semantic preservation during large -scale or iterative generation, leadin g to redundancy and instability. This work introduces a principled evaluation framework for large language model (LLM) based text augmentation, comprising two components: (1) Scalability Analysis, which measures semantic consistency as augmentation volume increases, and (2) Iterative Augmentation with Summarization Refinement (IASR), which evaluates semantic drift across recursive paraphrasing cycles. Empirical evaluations across state -of-the -art LLMs show that GPT-3.5 Turbo achieve d the best balance of semantic fidelity, diversity, and generation efficiency. Applied to a real -world topic modeling task using BERTopic with GPT-enhanced few -shot labeling, the proposed approach results in a 400% increase in topic granularity and complete elimination of topic overlaps. These findings validate d the utility of the proposed frameworks for structured evaluation of LLM -based augmentation in practical NLP pipelines.