Blending sentiment and topic analysis to explain discrete emotions from online fitness center customer reviews
DOI:
https://doi.org/10.47197/retos.v78.118658Keywords:
Emotions, fitness, machine learning, online reviews, stars-rankingAbstract
Introduction: Emotions elicited by services influence customer satisfaction and overall experience. Online reviews provide a valuable source for identifying these emotional patterns through text‑analysis techniques.
Objective: To examine how topic relevance and sentiment expressed in digital reviews predict discrete emotions among gym users, proposing an approach based on “polarized topics.”
Methodology: A total of 3,250 reviews from 38 Spanish gyms were analyzed. The study employed a mixed‑methods approach combining topic modeling using LDA, sentiment analysis with TextBlob, and machine‑learning algorithms (XGBoost), integrated with SHAP explainability techniques. The interaction between topics and sentiment polarity was used to predict ten discrete emotions, including joy, anger, sadness, and trust.
Results: Staff friendliness, value for money, and hygiene emerged as highly predictive topics. Positive evaluations of staff increased emotions such as joy and trust, whereas comments related to COVID‑related absences were associated with higher levels of anger and sadness. The “polarized topics” approach yielded strong emotional classification performance, achieving F1‑scores above 0.84 for most emotions.
Discussion: The findings show that the combination of topic modeling, sentiment analysis, and explainable AI enables precise identification of which service attributes trigger specific emotions, offering a useful interpretive framework for managers in the fitness sector.
Conclusions: The proposed method constitutes a scalable and transparent approach for predicting discrete emotions in reviews. Its application can support improvements in service design and emotional alignment in organizations oriented toward wellbeing.
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