基于LDA主题建模的旅游失败叙事挖掘与演变分析
Mining and Evolution Analysis of Tourism Failure Narratives Based on LDA Topic Modeling
DOI: 10.12677/sa.2026.157150, PDF,    科研立项经费支持
作者: 林晓慧:海南热带海洋学院旅游学院,海南 三亚;赵美玲:海岛旅游资源数据挖掘与监测预警技术文化和旅游部重点实验室,海南 三亚
关键词: 旅游失败LDA主题模型用户生成内容Tourism Failure LDA Topic Model User-Generated Content
摘要: 旅游体验研究长期聚焦“满意度”而忽视“失败”视角,导致对游客核心痛点的识别不足。本文特引入负面体验分析视角,以豆瓣“旅游失败小组”的用户生成内容为研究对象,运用LDA主题模型对“突发公共卫生事件期间”与“常态化时期”的旅游失败文本进行主题挖掘与对比分析。研究表明,后一时期(即常态化时期)旅游失败的主要矛盾已从应对外部风险,转变为管理系统内部的服务效能与协同韧性,标志着旅游体验叙事范式从“危机应对”向“压力管理”的根本转换。
Abstract: Tourism experience research has long predominantly concentrated on “satisfaction” while largely neglecting the “failure” perspective, resulting in an insufficient identification of tourists’ fundamental pain points. To address this gap, this study introduces a negative experience analysis framework. Utilizing user-generated content from the Douban “Travel Failure Group” as the research subject, it applies the LDA topic model to conduct thematic mining and a comparative analysis of tourism failure narratives from both the “public health emergency period” and “normalization period”. The findings indicate that in the latter period (i.e., the normalization period), the primary contradiction underlying tourism failure has shifted from responding to external risks to addressing the service efficacy and collaborative resilience within the management system. This transition signifies a fundamental paradigm shift in tourism experience narratives, moving from a “crisis response” model to a framework of “pressure management”.
文章引用:林晓慧, 赵美玲. 基于LDA主题建模的旅游失败叙事挖掘与演变分析[J]. 统计学与应用, 2026, 15(7): 65-74. https://doi.org/10.12677/sa.2026.157150

参考文献

[1] Kirilenko, A.P., Stepchenkova, S.O. and Dai, X. (2021) Automated Topic Modeling of Tourist Reviews: Does the Anna Karenina Principle Apply? Tourism Management, 83, Article 104241. [Google Scholar] [CrossRef
[2] Saoualih, A., Safaa, L., Bouhatous, A., Bidan, M., Perkumienė, D., Aleinikovas, M., et al. (2024) Exploring the Tourist Experience of the Majorelle Garden Using VADER-Based Sentiment Analysis and the Latent Dirichlet Allocation Algorithm: The Case of Tripadvisor Reviews. Sustainability, 16, Article 6378. [Google Scholar] [CrossRef
[3] 张莹莹, 陈恒宇, 张梦迪. 基于LDA模型的旅游住宿接待能力评价——以济南市为例[J]. 科技和产业, 2025, 25(6): 204-214.
[4] 涂晨, 李鑫, 叶程轶. 基于LDA主题模型与Apriori算法的旅游数据挖掘[J]. 物联网技术, 2023, 13(3): 108-112.
[5] 罗佳琦, 金贤珠, 黄松山, 等. 深度学习技术在旅游研究中应用的发展与展望[J]. 世界地理研究, 2025, 34(12): 188-200.
[6] 易柳夙, 陈晔, 王紫逸, 等. 花钱买罪受? 基于旅游者心理账户的旅游失败评价机制研究[J]. 旅游学刊, 2023, 38(7): 143-155.
[7] 姚延波, 吴艾凌, 刘亦雪, 等. “先共毁, 后共创”: 成年子女与父母旅游价值共毁恢复的实证研究[J]. 旅游科学, 2025, 39(11): 56-75.
[8] Casaló, L.V., Flavián, C., Guinalíu, M. and Ekinci, Y. (2015) Avoiding the Dark Side of Positive Online Consumer Reviews: Enhancing Reviews’ Usefulness for High Risk-Averse Travelers. Journal of Business Research, 68, 1829-1835. [Google Scholar] [CrossRef
[9] 朱张祥, 杨可宁, 许春晓, 等. 赔笑还是卖惨? 旅游在线投诉回复中的表情符号对游客宽恕的影响研究[J]. 信息系统学报, 2025(1): 68-88.
[10] Akarsu, T.N., Marvi, R. and Foroudi, P. (2022) Service Failure Research in the Hospitality and Tourism Industry: A Synopsis of Past, Present and Future Dynamics from 2001 to 2020. International Journal of Contemporary Hospitality Management, 35, 186-217. [Google Scholar] [CrossRef
[11] 粟路军, 贾伯聪. 旅游地危机事件研究回顾、述评与展望[J]. 湖南师范大学自然科学学报, 2023, 46(1): 57-69.