Understanding China’s Destination Image through TikTok Comments: Evidence from Sentiment and TF–IDF Analysis
DOI:
https://doi.org/10.61360/BoniGHSS262019740203Keywords:
inbound tourism, TikTok, user-generated content, sentiment analysis, destination image, China travelAbstract
In recent years, China has implemented a series of policies to promote the development of inbound tourism, and “China travel” has become a widely discussed topic on social media platforms. Using Python-based text mining and sentiment analysis techniques, this study analyzes user comments under the “#chinatravel” topic on TikTok to examine the emotional tendencies of international users toward tourism in China. The results indicate that positive comments significantly outnumber negative ones, suggesting that international users generally hold favorable attitudes toward traveling in China. TF–IDF analysis further shows that positive sentiments emphasize aesthetic appreciation, emotional resonance, and overall positive impressions, whereas negative sentiments are relatively limited and mainly associated with critical or politicized expressions. Overall, the findings reveal a distinct emotional bias in TikTok comment sections. User-generated comments serve as an important source of informational and emotional cues for potential tourists and provide empirical evidence of how online discourse shapes international perceptions of Chinese tourism destinations.
References
Aboalganam, K. M., AlFraihat, S. F., & Tarabieh, S. (2025). The impact of user-generated content on tourist visit intentions: The mediating role of destination imagery. Administrative Sciences, 15(4), 117. https://doi.org/10.3390/admsci15040117
An, S., Kim, W., Lee, B., & Suh, J. (2022). A study on the tourism-related information consumption process of tourists on social networking sites. Sustainability, 14(7), 3980. https://doi.org/10.3390/su14073980
China Daily. (2026, January 7). Polished inbound tourism sector sparkles brightly again. China Daily. https://www.chinadaily.com.cn/a/202601/07/WS695d9c6ea310d6866eb32574.html
China Daily. (2025, May 6). Exotic charm: East meets Middle East. China Daily. https://www.chinadaily.com.cn/a/202505/06/WS6819fc49a310a04af22bdbd6.html
Corso, F., Pierri, F., & De Francisci Morales, G. (2024, May). What we can learn from TikTok through its Research API. In Companion Publication of the 16th ACM Web Science Conference (pp. 110-114). https://doi.org/10.1145/3630744.3663611
Cui, J., Wang, Z., Ho, S. B., & Cambria, E. (2023). Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review, 56(8), 8469-8510. https://doi.org/10.1007/s10462-022-10386-z
Durachman, Y., Putra, S. J., Nanang, H., & Sukmana, H. T. (2024, August). Analysis sentiment of public opinion on social media using naïve Bayes and TF-IDF algorithms. In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCIT62134.2024.10701191
Guo, X., Pesonen, J., & Komppula, R. (2022). Analysing online travel reviews to identify temporal changes of a destination image. European Journal of Tourism Research, 32, 3209-3209. https://doi.org/10.54055/ejtr.v32i.2447
Havrlant, L., & Kreinovich, V. (2017). A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation). International Journal of General Systems, 46(1), 27-36. https://doi.org/10.1080/03081079.2017.1291635
Lestari, V. B., & Hutagalung, C. A. (2025). Evaluation of TF-IDF Extraction Techniques in Sentiment Analysis of Indonesian-Language Marketplaces Using SVM, Logistic Regression, and Naive Bayes. J-KOMA: Jurnal Ilmu Komputer dan Aplikasi, 8(1), 36-44. https://doi.org/10.21009/j-koma.v8i1.05
Liu, C., Jiang, M., & Muhammad, Z. A. (2024). The impact of TikTok short video factors on tourists’ behavioral intention among Generation Z and Millennials: The role of flow experience. Plos one, 19(12), e0315140. https://doi.org/10.1371/journal.pone.0315140
Mou, T., & Wang, H. (2025). Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism. Scientific Reports, 15(1), 1121. https://doi.org/10.1038/s41598-025-85139-3
Orea-Giner, A., Fuentes-Moraleda, L., Villacé-Molinero, T., Muñoz-Mazón, A., & Calero-Sanz, J. (2022). Does the implementation of robots in hotels influence the overall TripAdvisor rating? A text mining analysis from the industry 5.0 approach. Tourism Management, 93, 104586. https://doi.org/10.1016/j.tourman.2022.104586
Rita, P., Ramos, R., Borges-Tiago, M. T., & Rodrigues, D. (2022). Impact of the rating system on sentiment and tone of voice: A Booking. com and TripAdvisor comparison study. International Journal of Hospitality Management, 104, 103245. https://doi.org/10.1016/j.ijhm.2022.103245
Serrano-Malebrán, J., Campos-Núñez, F., Vidal-Silva, C., Veneros-Alquinta, D., Zuñiga-Contreras, F., & Ahumada-Gutierrez, D. (2025). From search engines to social influence: a stimulus–organism–response model of travel influencers on TikTok. Frontiers in Communication, 10, 1649647. https://doi.org/10.3389/fcomm.2025.1649647
Singgalen, Y. A. (2024). Sentiment Analysis and Trend Mapping of Hotel Reviews Using LSTM and GRU. Journal of Information Systems and Informatics, 6, 2814-2836. https://doi.org/10.51519/journalisi.v6i4.926
UN Tourism. (2026, January). World tourism barometer (Vol. 24, No. 1, excerpt). https://pre-webunwto.s3.eu-west-1.amazonaws.com/s3fs-public/2026-01/World_Tourism%20Barometer_Jan26_excerpt_v2.pdf?VersionId=u75u9KWPa6Dzc2CUHld7AvQ49FYrDTQC
Wang, H., & Yan, J. (2022). Effects of social media tourism information quality on destination travel intention: Mediation effect of self-congruity and trust. Frontiers in Psychology, 13, 1049149. https://doi.org/10.3389/fpsyg.2022.1049149
Wengel, Y., Ma, L., Ma, Y., Apollo, M., Maciuk, K., & Ashton, A. S. (2022). The TikTok effect on destination development: Famous overnight, now what?. Journal of Outdoor Recreation and Tourism, 37, 100458. https://doi.org/10.1016/j.jort.2021.100458
Wijaya, C. O., Wijaya, S., & Jaolis, F. (2025). The influence of social media content on attitude, destination image and intention of female Muslim travelers to visit halal destinations: comparison between UGC and FGC. Journal of Islamic Marketing, 16(2), 402-427. https://doi.org/10.1108/JIMA-08-2023-0235
Wu, J., & Yang, T. (2023). Service attributes for sustainable rural tourism from online comments: Tourist satisfaction perspective. Journal of Destination Marketing & Management, 30, 100822. https://doi.org/10.1016/j.jdmm.2023.100822
Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and information systems, 60(2), 617-663. https://doi.org/10.1007/s10115-018-1236-4
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