Sentiment Analysis of Online Customer Experiences in Hospitality

Sentiment analysis of online customer experiences in hospitality harnesses computational techniques to extract and quantify emotional and evaluative content from guest reviews, social‐media posts and electronic word of mouth. By deploying methods that range from lexicon‐based classification to deep learning architectures, researchers can map overall satisfaction, identify strengths and weaknesses in service delivery, and reveal nuanced emotional reactions to aspects such as cleanliness, staff interaction, amenities and dining. Aspect‐based sentiment analysis further refines this approach by linking sentiment polarity to discrete service attributes, enabling hoteliers to prioritise improvements and personalise offerings. The integration of topic modelling and network analysis with sentiment detection has yielded insights into evolving guest expectations and cultural variations, while explainable machine learning techniques ensure that model outputs remain transparent and actionable for managers. As online platforms proliferate globally, these tools underpin data‐driven strategies for reputation management, sustainable service design and competitive differentiation across diverse market segments.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Sentiment Analysis of Online Customer Experiences in Hospitality publication trend

The graph below shows the total number of articles in sentiment analysis of online customer experiences in hospitality across all publications each year (not limited to Nature Index journals).

Technical terms

Sentiment analysis: A computational method for detecting and categorising emotional tone in text, often as positive, negative or neutral.

Aspect-based sentiment analysis: A finer-grained approach that associates sentiment polarity with specific attributes or components of a service or product.

Structural topic modelling (STM): A probabilistic technique that uncovers latent thematic structures in large text corpora while accommodating document‐level metadata.

Explainable machine learning: Techniques that elucidate how predictive models arrive at decisions, enhancing transparency and trust in automated analysis.

XGBoost: An efficient implementation of gradient boosting algorithms used for high-performance supervised learning tasks on structured data.

Semantic network analysis: A method to map and quantify relationships among terms in text, revealing conceptual linkages and discourse structure.

References

1. Understanding value perceptions and propositions: A machine learning approach. Journal of Business Research (2023).
2. Explaining tourist revisit intention using natural language processing and classification techniques. Journal of Big Data (2023).
3. The Impact of Hotel Customer Experience on Customer Satisfaction through Online Reviews. Sustainability (2022).