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作 者:简洪波 陈坚[1] 吴晓桐 Jian Hongbo;Chen Jian;Wu Xiaotong(College of Traffic&Transportation,Chongqing Jiaotong University,Chongqing 400074,China)
出 处:《旅游论坛》2025年第2期17-28,共12页Tourism Forum
基 金:国家自然科学基金面上项目“融合社交网络信息的旅游出行影响机理及个性化行程链规划方法”(52472339)资助。
摘 要:情感分析是研究游客目的地感知形象的重要手段,分析过程的科学性和结果的准确性将对感知形象的建立产生关键影响。文章以上海迪士尼在线评论作为数据来源,运用LDA(latent Dirichlet allocation,潜在狄利克雷分布)模型构建游客感知形象维度,结合LCF-ATEPC(local context focused aspect term extraction and polarity classification,局部上下文聚焦的方面术语提取与极性分类)细粒度情感模型对游客情感进行深入分析,选择SnowNLP粗粒度情感模型作为对照,探究两类情感分析方法的差异。基于细粒度情感分析结果,运用词云图挖掘情感诱因。结果表明:游客评论蕴含多维度情感极性;细粒度和粗粒度模型分析结果在情感冲突维度具有明显差异;好玩、方便、合理和热心是游客感知形象正向情感诱因;排队频繁和时间过长是负面情感诱因,高温、寒冷和假期等外部环境在一定程度上加剧了负面情感反应。Sentiment analysis is an important tool to study tourists’perception of destination images.The scientific nature of the analysis process and the accuracy of the results significantly impact on the formation of these perceived images.This paper takes online reviews of Shanghai Disneyland as the data source,employs the LDA(latent dirichlet allocation)model to construct the dimensions of tourists’perceived images.It integrates the LCF-ATEPC(local context focused aspect term extraction and polarity classification)fine-grained sentiment model to conduct an in-depth analysis of tourists’sentiments.The SnowNLP(snow natural language processing)coarse-grained sentiment model is selected as a benchmark to explore the differences between the two types of sentiment analysis approaches.Based on the results of finegrained sentiment analysis,word cloud diagrams are used to identify emotional triggers.The results show that:tourist reviews contain multi-dimensional sentiment polarities.Significant differences are observed between the fine-grained and coarse-grained model results,especially in the dimension of sentiment conflict.Positive emotional triggers include fun,convenience,rationality and enthusiasm,while negative emotional triggers involve frequent and long queues,and external environmental factors,such as high temperature,cold weather,and holidays,exacerbate negative emotional reactions to a certain extent.
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