游客对干旱区景区气候感知的情感分析——以5A景区为例
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摘要
分析游客对气候变化的感知对旅游目的地可持续发展具有重要意义。通过获取2011年1月至2018年6月游客在新浪微博发布的有关干旱区5A级景点气候条件的50万余字评价文本,借助基于人工神经网络的情感分析法与扎根理论探讨了游客对气候情感的时空分异特征,并着重分析了低情感值的影响因素。结果发现:①气候情感整体值为0. 757,为"好"级;②从时间演变上看,情感值呈下降趋势,7年间的降幅为2. 46%,但情感值上升的景区数量与下降的景区数量基本持平;③从空间上看,低值区位于干旱区中心,高值区分布在东西两侧;④气候不佳、身心健康、景区受损、游览限制、交通受阻、期望差异6个维度构成了低情感值的影响因素。
Analysis of tourists' perception of climate change was important for the sustainable development of tourism destinations. Obtaining more than 500000 words of comments published by Sina Weibo about the climatic conditions of 5 A-level scenic spots in the arid area from January 2011 to June 2018,this paper explored the spatial and temporal differentiation characteristics of tourists' climate emotions with the help of sentiment analysis based on artificial neural network,and analyzed the influencing factors of low emotional value using grounded theory. The results showed that: ①The overall climate emotional value was 0. 757,which was a good grade. ②From the perspective of time evolution,the climate emotional value showed a downward trend,with a range of 2. 46% in 7 years,but the number of scenic spots with rising emotional value was the same as the number of scenic spots that were falling. ③From the spatial perspective,the low emotional value area was located in the center of the arid area,and the high value area was distributed in the east and west sides. ④The influencing factors was constituted by six dimensions,which were poor climate,physical and mental health,damage to scenic spots,restricted travel,blocked traffic,and expected differences.
Analysis of tourists' perception of climate change was important for the sustainable development of tourism destinations. Obtaining more than 500000 words of comments published by Sina Weibo about the climatic conditions of 5 A-level scenic spots in the arid area from January 2011 to June 2018,this paper explored the spatial and temporal differentiation characteristics of tourists' climate emotions with the help of sentiment analysis based on artificial neural network,and analyzed the influencing factors of low emotional value using grounded theory. The results showed that: ①The overall climate emotional value was 0. 757,which was a good grade. ②From the perspective of time evolution,the climate emotional value showed a downward trend,with a range of 2. 46% in 7 years,but the number of scenic spots with rising emotional value was the same as the number of scenic spots that were falling. ③From the spatial perspective,the low emotional value area was located in the center of the arid area,and the high value area was distributed in the east and west sides. ④The influencing factors was constituted by six dimensions,which were poor climate,physical and mental health,damage to scenic spots,restricted travel,blocked traffic,and expected differences.
引文
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[35]Kang S,Eltahir E A B.North China Plain Threatened by Deadly Heatwavesdue to Climate Change and Irrigation[J].Nature Communications,2018,9(1)∶1-9.
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[37]马丽君,孙根年,李馥丽,等.陕西省旅游气候舒适度评价[J].资源科学,2007,29(6)∶40-44.
[38]蒋贵彦,孙根年,王琳.青海省旅游气候舒适性评价及不利因素分析[J].干旱区资源与环境,2011,25(7)∶215-221.
[39]刘海洋,吴月,王乃昂,等.中国沙漠旅游气候舒适度评价[J].资源科学,2013,35(4)∶1-3.
[40]马天,李想,谢彦君.换汤不换药?游客满意度测量的迷思[J].旅游学刊,2017,32(6)∶53-63.
[41]张郴,黄震方,张捷,等.基于机器学习的南京市旅游地个性及其文化景观表征[J].地理学报,2017,72(10)∶1886-1903.
[42]Deng N,Li R.Feeling a Destination Through the“Right”Photos:A Machine Learning Model for DMOs'Photo Selection[J].Tourism Management,2018,65(4)∶267-278.
[2]Qiao G Q,Gao J.Chinese Tourists'Perceptions of Climate Change and Mitigation Behavior:An Application of Norm Activation Theory[J].Sustainability,2017,9(8)∶1-13.
[3]Becken S,Wilson J.The Impacts of Weather on Tourist Travel[J].Tourism Geographies,2013,15(4)∶620-639.
[4]Dubois G,Ceron J P,G9ssling S,et al.Weather Preferences of French Tourists:Lessons for Climate Change Impact Assessment[J].Climatic Change,2016,136(2)∶339-351.
[5]Tervokankare K,Hall C M,Saarinen J.Christmas Tourists'Perceptions to Climate Change in Rovaniemi,Finland[J].Tourism Geographies,2013,15(2)∶292-317.
[6]March H,SauríD,Llurdés J C.Perception of the Effects of Climate Change in Winter and Summer Tourist Areas:the Pyrenees and the Catalan and Balearic Coasts,Spain[J].Regional Environmental Change,2014,14(3)∶1189-1201.
[7]Pickering C M,Castley G,Burtt M R.Skiing Less Often in a Warmer World:Attitudes of Tourists to Climate Change in an Australian Ski Resort[J].Geographical Research,2010,48(2)∶137-147.
[8]Marshall N A,Marshall P A,Abdulla A,et al.Preparing for Climate Change:Recognising Its Early Impacts through the Perceptions of Dive Tourists and Dive Operators in the Egyptian Red Sea[J].Current Issues in Tourism,2011,14(6)∶507-518.
[9]Denstadli J M,Jacobsen J K S.More Clouds on the Horizon?Polar Tourists'Weather Tolerances in the Context of Climate Change[J].Scandinavian Journal of Hospitality&Tourism,2014,14(1)∶80-99.
[10]Xiang Z,Schwartz Z,Gerdes J H,et al.What Can Big Data and Text Analytics Tell Us about Hotel Guest Experience and Satisfaction?[J].International Journal of Hospitality Management,2015,44(1)∶120-130.
[11]Becken S,Stantic B,Chen J,et al.Connolly RM.Monitoring the Environment and Human Sentiment on the Great Barrier Reef:Assessing the Potential of Collective Sensing[J].Journal of Environmental Management,2017,203(1)∶87-97.
[12]Alaei A R,Becken S,Stantic B.Sentiment Analysis in Tourism:Capitalizing on Big Data[J].Journal of Travel Research,2017,56(9)∶1-14.
[13]Kirilenko A P,Stepchenkova S O,Kim H,et al.Automated Sentiment Analysis in Tourism:Comparison of Approaches[J].Journal of Travel Research,2017,56(10)∶1-14.
[14]Liu B,and Zhang L.A Survey of Opinion Mining and Sentiment A-nalysis[C].Mining Text Data.New York∶Springer,2012∶415-428.
[15]Feldman R.Techniques and Applications for Sentiment Analysis[J].Communications of the ACM,2013,56(4)∶82-89.
[16]Chiu C,Chiu N H,Sung R J,et al.Opinion Mining of Hotel Customer-generated Contents in Chinese Weblogs[J].Current Issues in Tourism,2015,18(5)∶477-495.
[17]刘逸,保继刚,朱毅玲.基于大数据的旅游目的地情感评价方法探究[J].地理研究,2017,36(6)∶1091-1105.
[18]Kirilenko,Stepchenkova.Sochi 2014 Olympics on Twitter:Perspectives of Hosts and Guests[J].Tourism Management,2017,63(6)∶54-65.
[19]邓宁,钟栎娜,李宏.基于UGC图片元数据的目的地形象感知---以北京为例[J].旅游学刊,2018,33(1)∶53-62.
[20]Markopoulos G,Mikros G,Iliadi A,et al.Sentiment Analysis of Hotel Reviews in Greek:A Comparison of Unigram Features[M].Cultural Tourism in a Digital Era.Springer International Publishing,2015∶373-383.
[21]Ye Q,Zhang Z,Law R.Sentiment Classification of Online Reviews to Travel Destinations by Supervised Machine Learning Approaches[J].Expert Systems with Applications,2009,36(3)∶6527-6535.
[22]Claster W B,Dinh H,Cooper M.Naive Bayes and Unsupervised Artificial Neural Nets for Cancun Tourism Social Media Data Analysis[C].Proceedings of the Second World Congress on Nature and Biologically Inspired Computing.New York:IEEE,2010∶158-163.
[23]Kirilenko A P,Molodtsova T,Stepchenkova S O.People as Sensors:Mass Media and Local Temperature Influence Climate Change Discussion on Twitter[J].Global Environmental Change,2015,30(12)∶92-100.
[24]Saura J R,Palossanchez P,Martin M A R.Attitudes Expressed in Online Comments about Environmental Factors in the Tourism Sector:An Exploratory Study[J].International Journal of Environmental Research&Public Health,2018,15(3)∶553.
[25]Sofield T,Li S.Tourism Governance and Sustainable National Development in China:A Macro-level Synthesis[J].Journal of Sustainable Tourism,2011,19(4-5)∶501-534.
[26]陈亚宁,方功焕,王怀军,等.西北干旱区气候变化对水文水资源影响研究进展[J].地理学报,2014,69(9)∶1295-1304.
[27]Kontopoulos E,Berberidis C,Dergiades T,et al.Ontology-based Sentiment Analysis of Twitter Posts[J].Expert Systems with Applications,2013,40(10)∶4065-4074.
[28]Confente I.Twenty-five Years of Word-of-mouth Studies:A Critical Review of Tourism Research[J].International Journal of Tourism Research,2014,17(6)∶613-624.
[29]United States Environmental Protection Agency.Glossary of Climate Change Terms[OL].2013,http://www.epa.gov/climate change/glossary.html.
[30]张存杰,廖要明,段居琦,等.我国干湿气候区划研究进展[J].气候变化研究进展,2016,12(4)∶261-267.
[31]Schmunk S,H9pken,Wolfram,Fuchs M,et al.Sentiment Analysis:Extracting Decision-relevant Knowledge from UGC[C].Information and Communication Technologies in Tourism 2014.Springer International Publishing,2014∶253-265.
[32]Yan Q,Zhou S,Wu S.The Influences of Tourists'Emotions on the Selection of Electronic Word of Mouth Platforms[J].Tourism Management,2018,66(6)∶348-363.
[33]程励,张同颢,付阳.城市居民雾霾天气认知及其对城市旅游目的地选择倾向的影响[J].旅游学刊,2015,30(10)∶37-47.
[34]易善君,李君轶,李秀琴,等.基于微博大数据的空气质量与居民情感相关性对比研究---以西安市和上海市为例[J].干旱区资源与环境,2017,31(5)∶39-44.
[35]Kang S,Eltahir E A B.North China Plain Threatened by Deadly Heatwavesdue to Climate Change and Irrigation[J].Nature Communications,2018,9(1)∶1-9.
[36]李山,孙美淑,张伟佳,等.中国大陆1961-2010年间气候舒适期的空间格局及其演变[J].地理研究,2016,35(11)∶2053-2070.
[37]马丽君,孙根年,李馥丽,等.陕西省旅游气候舒适度评价[J].资源科学,2007,29(6)∶40-44.
[38]蒋贵彦,孙根年,王琳.青海省旅游气候舒适性评价及不利因素分析[J].干旱区资源与环境,2011,25(7)∶215-221.
[39]刘海洋,吴月,王乃昂,等.中国沙漠旅游气候舒适度评价[J].资源科学,2013,35(4)∶1-3.
[40]马天,李想,谢彦君.换汤不换药?游客满意度测量的迷思[J].旅游学刊,2017,32(6)∶53-63.
[41]张郴,黄震方,张捷,等.基于机器学习的南京市旅游地个性及其文化景观表征[J].地理学报,2017,72(10)∶1886-1903.
[42]Deng N,Li R.Feeling a Destination Through the“Right”Photos:A Machine Learning Model for DMOs'Photo Selection[J].Tourism Management,2018,65(4)∶267-278.