Natural Language Processing for Guest Experience and Sentiment Analysis in Hospitality

The hospitality industry increasingly relies on customer feedback to evaluate service quality and improve guest satisfaction. Hotel reviews contain valuable unstructured textual data that reflects customer opinions, emotions, expectations, and overall experiences. However, manual analysis of large volumes of reviews is time-consuming, inconsistent, and susceptible to human bias. To address these challenges, this study presents a real-time sentiment and emotion analysis system that leverages Natural Language Processing (NLP), machine learning, and deep learning techniques to automatically interpret guest feedback. The proposed system performs comprehensive text preprocessing, including tokenization, stop-word removal, lemmatization, punctuation filtering, and text normalization, to improve data quality. Feature extraction is achieved through TF-IDF and advanced word embedding techniques such as Word2Vec, GloVe, and BERT. A hybrid sentiment-emotion classification framework integrating machine learning algorithms, including Support Vector Machine (SVM) and Random Forest, with deep learning models such as Long Short-Term Memory (LSTM) networks and BERT Transformers is employed to achieve high classification accuracy. In addition, the system performs aspect-based sentiment analysis to evaluate specific service components, including staff behavior, cleanliness, and amenities, while also providing emotion distribution visualization and trend analysis over time. By transforming unstructured customer reviews into meaningful emotional and sentiment insights, the proposed framework enables hotel management to identify service strengths and weaknesses, detect negative feedback at an early stage, enhance customer satisfaction, and support data-driven decision-making. The system demonstrates the potential of AI-driven text analytics to improve service quality and operational efficiency in the hospitality sector.