Enhancing the Post-COVID Tourism Competitiveness Strategy: Fuzzy Logic-Enhanced Deep Learning for Sentiment Analysis
Zhenghai Ai, Hong Su, Yong Qin, Yuyan Luo, Marinko Škare
Keywords:
Post-COVID tourism, sentiment analysis, fuzzy logic, deep learning, tourist behavior, qualitative comparative analysis
Abstract:
Amid the digital transformation of the global tourism sector, understanding tourist behavior and preferences is critical for enhancing tourism competitiveness, particularly in the post-COVID-19 context. This study integrates machine learning–based sentiment analysis with configurational methods to examine how tourism destinations achieve high competitiveness. Focusing on 34 5A-level tourism destinations in China, we analyze a large corpus of tourist-generated content to identify key destination attributes and assess their performance. Using topic modeling and sentiment analysis, we extract core attributes reflecting tourists’ concerns and experiences. We then apply fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA) to uncover configurational pathways leading to high tourism competitiveness. The results reveal that no single attribute is sufficient on its own; instead, multiple configurations of destination attributes jointly drive competitiveness. Specifically, five distinct configurational models are identified, representing alternative “recipes” for achieving high-performing and competitive destinations. This study contributes a novel analytical framework that bridges data-driven sentiment analysis and causal configurational logic. The findings provide actionable insights for destination managers to design targeted strategies that enhance both tourist satisfaction and competitiveness. Beyond the Chinese context, the proposed framework is transferable to other tourism settings, offering a scalable approach for analyzing destination competitiveness in the digital era.
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 Enhancing the Post-COVID Tourism Competitiveness Strategy: Fuzzy Logic-Enhanced Deep Learning for Sentiment Analysis [PDF file] [Filesize: 815.88 KB]
 10.7441/joc.2026.02.13
Ai, Z., Su, H., Qin, Y., Luo, Y., & Škare, M. (2026). Enhancing the Post-COVID Tourism Competitiveness Strategy: Fuzzy Logic-Enhanced Deep Learning for Sentiment Analysis. Journal of Competitiveness 18(2), 340–360. https://doi.org/10.7441/joc.2026.02.13
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