基于大数据的旅游目的地情感评价方法探究
发布时间：2019-05-03 18:32
【摘要】：基于情绪分类取向,通过界定三个旅游文本情感分析的过滤参数:旅游专属词库、语义逻辑规则和情感乘数,构建基于网络大数据的旅游目的地情感评价模型。基于该模型,抓取了120731条游客评论对8个旅游目的地进行评价,并以联合国世界旅游组织旅游可持续发展监测数据作为标准数据进行校验。研究证实三个过滤参数具有一定的科学性,能够较为准确地捕捉到游客对目的地评价的总体情感意象;经过单年度和多年度校验,六类规则的准确度依次为:C2C1C3B评分法A,即规则C2下的评价结果与监测结果最为吻合。结论证实了旅游大数据的可用性,为后续的理论推进和实践应用提供了科学依据。
[Abstract]:Based on emotion classification orientation, by defining three filter parameters of tourism text emotion analysis: tourism-specific lexicon, semantic logic rules and emotion multiplier, this paper constructs a tourism destination emotion evaluation model based on network big data. Based on the model, 120731 tourist comments were captured to evaluate 8 tourist destinations, and the monitoring data of tourism sustainable development of the United Nations World Tourism Organization (WTO) was used as the standard data for verification. The research proves that the three filtering parameters are scientific and can accurately capture the overall emotional image of tourists' evaluation of destination. After single-year and multi-year verification, the accuracy of the six types of rules is as follows: C2C1C3B scoring method A, that is, the evaluation results under rule C2 are the most consistent with the monitoring results. The conclusion confirms the usability of tourism big data and provides a scientific basis for the follow-up theoretical advancement and practical application.
【作者单位】： 中山大学旅游学院;
【基金】：教育部人文社会科学研究青年基金项目(14YJC790083) 国家自然科学基金项目(41571137)
【分类号】：F591
本文编号：2469157
[Abstract]:Based on emotion classification orientation, by defining three filter parameters of tourism text emotion analysis: tourism-specific lexicon, semantic logic rules and emotion multiplier, this paper constructs a tourism destination emotion evaluation model based on network big data. Based on the model, 120731 tourist comments were captured to evaluate 8 tourist destinations, and the monitoring data of tourism sustainable development of the United Nations World Tourism Organization (WTO) was used as the standard data for verification. The research proves that the three filtering parameters are scientific and can accurately capture the overall emotional image of tourists' evaluation of destination. After single-year and multi-year verification, the accuracy of the six types of rules is as follows: C2C1C3B scoring method A, that is, the evaluation results under rule C2 are the most consistent with the monitoring results. The conclusion confirms the usability of tourism big data and provides a scientific basis for the follow-up theoretical advancement and practical application.
【作者单位】： 中山大学旅游学院;
【基金】：教育部人文社会科学研究青年基金项目(14YJC790083) 国家自然科学基金项目(41571137)
【分类号】：F591
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