Sentiment analysis of social media platforms has become an important method for examining tourists' perceptions and behavioral patterns. This study provides a systematic review of tourism destination research applying sentiment analysis to social media data. It identifies methodological trends, highlights underutilized platforms and analytical approaches, and outlines directions for future research. A systematic review of studies published between 2016 and 2024 was conducted using major academic databases, including ScienceDirect, IEEE Xplore, Emerald Insight, Springer, Scopus, Google Scholar, and Web of Science. Following a structured screening process, 30 relevant studies were selected for detailed analysis. The findings indicate a gradual shift from lexicon-based methods to machine learning and transformer-based models, although hybrid approaches remain limited. Research relies heavily on platforms such as X (formerly Twitter) and TripAdvisor, while visually oriented platforms such as YouTube, Instagram and TikTok are comparatively underexplored. Overall, the evidence confirms that social media sentiment analysis contributes significantly to understanding destination image formation and visitor evaluation. However stronger theoretical integration and broader data representation are needed to advance the field.
Syed Shah (Thu,) studied this question.