旅游景点间细粒度语义交互作用挖掘及模式分析：以云南省为例

研究旅游景点语义交互及交互作用模式,对根据游客需求优化旅游格局有重要意义。现有语义交互挖掘方法忽略了文本中包含人感知信息的上下文词汇;此外,缺少以景点交互为单位分析交互作用模式的研究。为此,本文提出了一个景点间细粒度语义交互作用挖掘和模式分析框架。首先抽取文本中景点交互的语境;然后利用TF-IDF关键词抽取和语义网络分析方法,从讨论焦点和语义结构角度挖掘景点间细粒度的语义交互作用;最后结合Spearman秩相关系数、Graph Kernel图相似度度量方法和网络分析方法,分析语义交互作用模式。以云南省2018年游记数据进行实例分析,结果表明：① 利用本文提出的框架可以挖掘和分析各个景点间细粒度的语义交互作用,辅助有关部门结合游客意见提升旅游体验;可以分析语义交互作用模式,发现优化旅游格局的关键路线片段;② 苍山-洱海应着重提升自然风光体验;而大理古城-洱海应考虑改善游客对品牌旅游资源关注不足的问题;③ 云南省单核心集聚型、单核心辐射型、多区域合作型景点语义交互模式共存,呈现出点轴渐进扩散特征。可利用中介中心性较高且跨区域的景点交互,推动其他2种模式向多区域合作型转化,推进全域旅游战略实施。本文研究可为旅游路线推荐以及平衡旅游格局提供参考。

Fine-grained Semantic Interaction Mining and Pattern Analysis between Tourist Attractions: A Case Study of Yunnan Province,China

Exploring the semantic interaction and interaction pattern of tourist attractions is useful for optimizing the tourism pattern according to the needs of tourists. Existing semantic interaction mining methods ignore the contextual vocabulary that contains human perception information in texts. And there is a lack of research that analyzes the interaction pattern. Therefore, this paper proposes a framework for fine-grained semantic interaction mining and pattern analysis between attractions. First, the contextual information between two attractions is extracted through the co-occurrence relationship of words based on the online travel notes. Then, the semantic connection between attractions is mined by using the method of keyword analysis based on TF-IDF and the method of semantic network analysis from the perspectives of discussion focus and semantic structure. Finally, we regard attraction interaction as an object and use the Spearman rank correlation coefficient and the Graph Kernel (a method for graph similarity measurement) to calculate the correlation between them. Then the network analysis method is used to explore the interaction pattern. The experiment takes Yunnan Province as the case study area, the results of the text mining using travel notes in 2018 show that: (1) The framework is feasible and applicable. The travel experience can be improved according to the needs of tourists by mining and analyzing the fine-grained semantic interaction between attractions. And the route fragments that play a key role in optimizing the tourism pattern can be found by analyzing the semantic interaction pattern of attractions; (2) Cangshan Mountain-Erhai Lake should focus on improving the natural scenery travel experience; while Dali Old Town-Erhai Lake should consider improving tourists’ insufficient attention to branded tourism resources; (3) The coexistence of the three types of semantic interaction patterns, including single-core agglomeration, single-core radial, and multi-regional cooperation, presents the characteristics of node-axes evolving and diffusing. The high betweenness centrality and cross-regional attraction interactions are important for promoting the transformation of the other two models to multi-regional cooperation to develop "global tourism". The research results can provide references for recommending tourism routes and balancing tourism patterns. In the future work, we will explore the dynamic evolution of the semantic interaction between attractions and apply the results to tourist route recommendation.