A Reproducible Indonesian NLP Pipeline for Multiclass Sentiment Classification of Hospitality Reviews

Online hospitality reviews provide valuable insights into guest experiences, service quality, and operational performance. However, the unstructured and noisy nature of review text makes large-scale analysis difficult, especially for Indonesian-language reviews that often contain informal expressions, abbreviations, spelling variations, and inconsistent sentence structures. Although sentiment analysis has been widely applied in hospitality research, studies focusing on Indonesian-language hospitality reviews remain limited, and few have presented a reproducible Natural Language Processing (NLP) workflow for multiclass sentiment classification. This study proposes a reproducible Indonesian NLP pipeline for classifying hospitality reviews into positive, neutral, and negative sentiment categories. The workflow integrates review collection, sentiment annotation, Indonesian text preprocessing, TF-IDF feature extraction, and supervised classification using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. The dataset consists of 450 Indonesian-language hotel reviews collected from Google Reviews across three hotel segments: budget, mid-scale, and upscale. The experimental results show that SVM achieved the best overall performance, with 91.78% accuracy, 91.35% precision, 91.78% recall, and 91.50% F1-score, outperforming Naïve Bayes and Logistic Regression under the same experimental setting. These findings indicate that classical machine learning, when supported by systematic preprocessing and consistent feature representation, remains highly effective for Indonesian hospitality review analytics. This study contributes a practical and reproducible baseline for Indonesian-language sentiment classification and provides a foundation for future intelligent review monitoring systems in the hospitality sector.