The integration of technology, particularly social media and user-generated content platforms, has shaped tourist behavior and decision-making processes. Tourists increasingly rely on online reviews or electronic word of mouth (eWOM) as key determinants in destination perception and selection. Sentiment analysis using big data and text mining techniques is conducted to understand and harness the power of eWOM in shaping tourist experiences. Bibliometric analysis is used in research to identify potential citation and co-citation patterns, facilitating exploration and explanation of key research content in specific fields. This study conducts bibliometric analysis on a collection of Scopus articles related to satisfaction and user reviews in the field of tourism using big data and text mining techniques. The results from analyzing 425 articles show that Asia is the most prominent region for this field of research. Citation analysis resulted in 10 clusters, whereas bibliometric coupling resulted in 6 clusters, with 1 cluster having newly emerging topics in this field of research, namely experience and cultural tourism. Keywords in this field of research are separated into 5 clusters, with "sustainable tourism," "topic modeling," and "hotel industry" being the newly emerging keywords in this field of research.
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