"Dialogue Between Pastoralism and Civilization": An Analysis of Cognition and Emotion Toward Traditional Chinese Culture in Li Ziqi's Videos Based on YouTube Comments
DOI:
https://doi.org/10.54097/pey5em63Keywords:
Li Ziqi; traditional Chinese culture; cross-cultural communication; LDA topic model; sentiment analysis; YouTube comments; natural language processing.Abstract
This study takes English comments on Li Ziqi’s videos featuring traditional crafts on YouTube as its research object, employing a combined method of LDA topic modeling and sentiment analysis to systematically explore the cognitive structure and emotional responses of overseas audiences toward traditional Chinese culture. The findings reveal that Li Ziqi’s videos, through their "pastoral idyll" lifestyle imagery and Eastern female representations, have triggered a strong resonance with overseas viewers regarding Chinese culture. The comment topics are concentrated in areas such as natural life, femininity, and cultural identity, with an overall positive emotional tendency. Furthermore, the paper uncovers the intrinsic mechanism linking audience cognition and emotional responses, emphasizing the critical role of emotional resonance in cross-cultural communication. The conclusions provide new empirical support and strategic insights for the international dissemination of Chinese culture.
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References
[1] Wang, J. J. (2021). Exploring the overseas communication of Chinese culture in the new media environment: Reflections on the popularity of "Lao Wai Chris" [J]. Popular Literature and Art, (13), 75–76.
[2] Zhang, L., Deng, Y. L., & Wu, Y. (2021). New approaches to the international communication of Chinese culture in the age of social media [J]. International Communication, (02), 14–17.
[3] Zhang, H. Y. (2020). Practical experience and feasible paths for the external communication of Chinese culture: A case study of Li Ziqi’s short videos on YouTube [J]. Publication Horizon, (12), 82–84.
[4] Yuan, Y., & Bai, Z. (2020). Implications of cross-cultural communication in self-media for international Chinese language promotion: A case study of Li Ziqi’s works [J]. Chinese Character Culture, (15), 96–98.
[5] Zhan, X., & Li, H. J. (2021). Cross-cultural communication of Tibetan-related self-media on YouTube: A case study of "Tibet Travel" [J]. Journal of Ethnology, (03), 113–121.
[6] Yang, W. J. (2022). A study on self-media communication of foreign nationals in China from the perspective of cross-cultural communication: Taking "foreign internet celebrities" telling Chinese stories as an example [J]. Southeast Communication, (01), 46–48.
[7] Wang, G. H., Lu, J. L., & Liu, M. H. (2022). Cultural misinterpretation in new media cross-cultural communication and its countermeasures [J]. International Communication, (03), 36–39.
[8] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation [J]. Journal of Machine Learning Research, 3, 993–1022.
[9] Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415–463).
[10] Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis [J]. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
[11] Cambria, E., Poria, S., Gelbukh, A., et al. (2017). Sentiment analysis is a big suitcase [J]. IEEE Intelligent Systems, 32(6), 74–80.
[12] Liu, Y. W., & Wei, Y. (2020). Research on sentiment analysis based on LDA topic model [J]. Electronic Science and Technology, 33(07), 12–16+26.
[13] Bai, J., & Hong, X. J. (2022). Text mining and sentiment analysis of online public opinion based on bullet comments [J]. Software Engineering, 25(11), 44–48.
[14] Zhang, Y., & Qiu, H. F. (2023). Public perception of cultural services in lake park ecosystems based on LDA topic model [J]. Chinese Landscape Architecture, 39(07), 121–126. DOI:10.19775/j.cla.2023.07.0121.
[15] Sun, L. L., Hu, Y. R., & Liu, H. J. (2021). Research on user focus in online brand communities based on LSTM-LDA algorithm and IPA analysis [J]. Journal of Intelligence, 40(09), 178–186.
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