Appraising Customer Experience from Hotel Reviews using Text-Based Transformers

In the rapidly evolving landscape of the hospitality industry, comprehending and enhancing customer experience through digital feedback has become a strategic imperative. This study introduces a high-precision framework powered by advanced transformer-based architectures to evaluate customer sentiments in hotel reviews. A significant challenge in this domain, class imbalance, was addressed through an innovative data augmentation strategy utilizing ChatGPT to generate semantically consistent paraphrased samples for minority classes. We conducted a comprehensive comparative analysis of five state-of-the-art models: BERT, DistilBERT, RoBERTa, ELECTRA, and GPT-2. The methodology was validated using a specialized dataset, the Bali Hotel Review Corpus (BHRC), alongside four globally recognized benchmarks (IMDb, SST-1, SST-2, and MR). Experimental results demonstrate that the proposed framework achieves exceptional accuracy levels, with the ELECTRA model outperforming others by reaching a peak accuracy of 99.96% under balanced conditions. This performance is attributed to ELECTRA’s efficient discriminative pre-training objective, which proves superior to standard masked or generative approaches for domain-specific sequence classification. Our findings provide a robust and resource-efficient benchmark for real-time customer experience appraisal, offering hotel managers actionable insights to drive service quality in the modern digital tourism economy.