旅游舆情是旅游大数据最重要的呈现形式，对旅游市场营销、目的地监管、政策制定尤其是旅游危机沟通都具有重要价值。高效可靠领域适配的旅游舆情自动内容分析方法是利用海量数据进行领域问题研究的前提条件和关键所在，具有重要的方法学研究价值。此外，在计算社会科学范式下利用数据驱动的研究方式，探究得到的旅游危机沟通的长期动态规律对旅游危机的有效应对具有重要的理论价值和实践指导意义。本项目利用2003年以来旅游相关的新闻、微信公众号、微博和游客评价数据，综合运用元学习框架、文本挖掘、机器学习、统计和时间序列分析方法，来探究情感分析模型设计的全空间交互影响作用和系统性提升分析准确性的方法；构建降低人工参与来提升主题分析可解释性的方法；进而识别多类旅游危机情景要素，揭示各要素对沟通效果的影响、调节作用和交互机制，以及旅游危机舆情的跨媒体传导关系。最终满足计算社会科学范式的核心要求，形成计算旅游研究的完整闭环。

Tourism media is the most important carrier of tourism big data, playing an important role in marketing, destination supervision, policymaking, and especially in tourism crisis communication. Efficient, reliable and domain-adapted Automated Content Analysis (ACA) for tourism media is indispensable and critical to examine specific domain research questions by using tourism big data. Accordingly, it has considerable methodological value in tourism research. In addition, long-term and dynamic patterns discovered by using a data-driven method in computational social science, have sound theoretical and practical value for tourism crisis communication. In this research, tourism-related news, articles in WeChat, Weibo, and tourist reviews published since 2003, are employed as data sources. We adopt the meta-learning framework, text mining, machine learning, statistics and time series analysis methods to conduct the following studies: First, we investigate the interaction effects of the full spaces in sentiment analysis model design, in order to systematically improve the accuracy of the analysis. Second, we propose a topic analysis method to reduce manual work and improve the interpretability of the results. Third, we identify multiple situational elements of tourism crisis communication. Subsequently, the main effects, moderating effects, and interaction effects of the elements are revealed, as well as the cross-media communication mechanism of the tourism crisis. In summary, this research demonstrates a closed-loop for conducting computational tourism research, which fulfills the key requirements of computational social science.