Artificial Intelligence and Synthetic Data: A Natural Language Processing Protocol for Synthetic Data Augmentation with Human Validation in Sensitive Domains

Research on sensitive human narratives is increasingly constrained by ethical and privacy regulations that limit access to primary data, creating a structural small-data challenge that limits deep computational analysis. To address this limitation, this study validates a Natural Language Processing protocol that scales 946 real breakup narratives from r/breakups to 6000 human-validated high-fidelity synthetic records across five BERTopic clusters. The architecture employs MPNet, UMAP, and HDBSCAN to map latent space and thematically cluster texts, extracts seed documents using the Kneedle algorithm, and orchestrates DeepSeek V3.2 with stochastic sampling and small batches (k = 5). Automated validation via Cosine Similarity with a P10 threshold attained a mean semantic similarity of 0.7204 (range 0.6413–0.7855) and a fidelity rate of 99.08%. Expert human review by two researchers of this investigation evaluated 1732 posts on topic adherence and emotional authenticity using Gwet’s AC2. Five of six clusters achieved AC2 ≥ 0.70 on both dimensions; Topic 3 showed marginal adherence (AC2 = 0.660) while maintaining acceptable authenticity (AC2 = 0.817), and the 1200 synthetic posts for Topic 5 failed human validation (AC2 < 0.50) due to documented LLM safety-filter limitations and are excluded from the final corpus. These results demonstrate that the proposed protocol enables the research community to generate validated, privacy-preserving synthetic data ecosystems while establishing empirical boundary conditions for sensitive topic analysis.