What question did this study set out to answer?

To present a framework for analyzing hotel customer reviews using natural language processing and machine learning.

Beyond the stars: predicting guest satisfaction through NLP and machine learning of hotel reviews

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Framework demonstrates improved customer satisfaction insights through sentiment analysis in hotel reviews, suggesting better service recovery.

To present a framework for analyzing hotel customer reviews using natural language processing and machine learning.
Developed a supervised machine learning model with over 500,000 hotel reviews
Applied comprehensive data preprocessing including tokenization, stop-word removal, and lemmatization
Used sentiment analysis scores along with Doc2Vec and TF-IDF for feature engineering
Evaluated model performance with precision–recall curve and average precision metrics
Random forest classifier achieved an average precision of 0.37
Sentiment analysis features were most influential in predicting customer satisfaction
The model effectively identifies negative reviews for proactive management

Fathy et al. (2026) studied this question.

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Framework demonstrates improved customer satisfaction insights through sentiment analysis in hotel reviews, suggesting better service recovery.