The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This wor...The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This work takes this model established by the work of Dr.Babak Taheri in 2018 on Monuments World Heritage of UNESCO,adopting an alternative data collection method to the face-to-face survey.Therefore,this work takes as a source of data the reviews collected in the recommendation platform TripAdvisor,working the same constructions of the MTE,with the collection of similar terms and the relationships between them.In order to highlight the terms,a first step is established with the use of Natural Language Processing(NLP),followed by the use of Machine Learning(ML)techniques to generate the relationships between the constructors defined in the models.The study makes a comparison using the method,in immaterial nature such as a flamenco show in the city of Seville;Flamenco has been declared by UNESCO an intangible World Heritage Site since 2010.The results of the study go in two directions:on the one hand to find similarities in the study of the specific MTE of both monuments with the hypotheses worked in the original model of Taheri.In addition to highlighting possible distinctive elements of each case and,and furthermore within the value contribution of the visit when it is led by an official tour guide,on the other hand,give presence to the model of obtaining data by reviews as a complementary data source of any tourist study.The data collection and analysis from both NLP and ML techniques permit the scientific study and the tourist operators to develop better value propositions to users and understanding of heterogeneous behaviors in the tourism industry.The study of reviews within the MTE allows identifying the stimulus that leads the user to choose an activity and hire it.These studies are extendable to other industries and business models,given the importance that references acquire within the consumer willing to buy.For the scientific community,the use of ML is a solid way to initiate studies on behavioral models,supplement them,and accept or reject hypotheses.When the source of the data is taken from free expressions,such as reviews,the appearance of bias in the behavior is attenuated.展开更多
Among social media networks,TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations.Sentiment analysis is amethod that can b...Among social media networks,TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations.Sentiment analysis is amethod that can be used to analyze people's behaviors and opinions onpublic and socialmedia platforms.In this study,hotel reviews are extracted fromthe five most attractive Sri Lankan cities,and user-written reviews are compared over user bubble ratings,which define overall travelers'experiences as a numerical scale that ranks from 1 to 5.We find that the compatibility between userwritten reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users'genuine opinions expressed in their reviews.To address this problem,a two-phase approach is proposed:(1)the ensemblemethod to improve the performance of lexicon-based outputs and identify the correctlymatching user review and bubble rating;(2)the self-learning approach to finding the sentiment of a review that does not properly label by the user.The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposedmodel.For example,regardless of bigram“not good”,the average percentages of the word“good”for each negatively identified review from the proposed model and bubble rating are 25.63%and 38.85%,respectively.Thereby,it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.展开更多
Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale buildin...Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.展开更多
This study proposes a methodology to detect weaknesses and strengths of a heritage destination.This methodology is based on the analysis of comments and opinions published by visitors on travel blogs and TripAdvisor.F...This study proposes a methodology to detect weaknesses and strengths of a heritage destination.This methodology is based on the analysis of comments and opinions published by visitors on travel blogs and TripAdvisor.For the content analysis,the NVivo software has been used.The content analysis allows the identification of key aspects of the experience of tourists.The semantic network graphically shows the strengths and weaknesses.The research was carried out at two different time points,which have allowed to show,on the one hand,the relevance that the state of conservation of heritage destination has in the tourist experience and,on the other hand,that the proposed methodology helps managers of the heritage destination to improve the cultural tourist’s experience with the destination.展开更多
阅读理解+阅读七选五(一)阅读理解A Gardens by the Bay Gardens by the Bay is the most popular attraction on TripAdvisor and it isn't hard to understand why.This place is mind-blowing and unlike anything we've eve...阅读理解+阅读七选五(一)阅读理解A Gardens by the Bay Gardens by the Bay is the most popular attraction on TripAdvisor and it isn't hard to understand why.This place is mind-blowing and unlike anything we've ever seen.If you have time for just one attraction in Singapore,then this should be it.You can buy tickets at the gate but you can get a discount if you buy them in advance through Klook or Get Your Guide.展开更多
文摘The Memorable Tourist Experience(MTE)is a scientific concept within the studies on tourism that is developed based on several related constructions:Perceived Confidence,Sincerity,Authenticity,and Satisfaction.This work takes this model established by the work of Dr.Babak Taheri in 2018 on Monuments World Heritage of UNESCO,adopting an alternative data collection method to the face-to-face survey.Therefore,this work takes as a source of data the reviews collected in the recommendation platform TripAdvisor,working the same constructions of the MTE,with the collection of similar terms and the relationships between them.In order to highlight the terms,a first step is established with the use of Natural Language Processing(NLP),followed by the use of Machine Learning(ML)techniques to generate the relationships between the constructors defined in the models.The study makes a comparison using the method,in immaterial nature such as a flamenco show in the city of Seville;Flamenco has been declared by UNESCO an intangible World Heritage Site since 2010.The results of the study go in two directions:on the one hand to find similarities in the study of the specific MTE of both monuments with the hypotheses worked in the original model of Taheri.In addition to highlighting possible distinctive elements of each case and,and furthermore within the value contribution of the visit when it is led by an official tour guide,on the other hand,give presence to the model of obtaining data by reviews as a complementary data source of any tourist study.The data collection and analysis from both NLP and ML techniques permit the scientific study and the tourist operators to develop better value propositions to users and understanding of heterogeneous behaviors in the tourism industry.The study of reviews within the MTE allows identifying the stimulus that leads the user to choose an activity and hire it.These studies are extendable to other industries and business models,given the importance that references acquire within the consumer willing to buy.For the scientific community,the use of ML is a solid way to initiate studies on behavioral models,supplement them,and accept or reject hypotheses.When the source of the data is taken from free expressions,such as reviews,the appearance of bias in the behavior is attenuated.
文摘Among social media networks,TripAdvisor acts as the main role because everyone is eager to share and review their thoughts on their travel experiences in different destinations.Sentiment analysis is amethod that can be used to analyze people's behaviors and opinions onpublic and socialmedia platforms.In this study,hotel reviews are extracted fromthe five most attractive Sri Lankan cities,and user-written reviews are compared over user bubble ratings,which define overall travelers'experiences as a numerical scale that ranks from 1 to 5.We find that the compatibility between userwritten reviews and bubble ratings has a low correlation because bubble ratings may not represent the overall idea of users'genuine opinions expressed in their reviews.To address this problem,a two-phase approach is proposed:(1)the ensemblemethod to improve the performance of lexicon-based outputs and identify the correctlymatching user review and bubble rating;(2)the self-learning approach to finding the sentiment of a review that does not properly label by the user.The performance is studied by considering reviews incompatible with the sentiment of user bubble rating and the sentiment generated by the proposedmodel.For example,regardless of bigram“not good”,the average percentages of the word“good”for each negatively identified review from the proposed model and bubble rating are 25.63%and 38.85%,respectively.Thereby,it is apparent that the negative sentiments derived by bubble rating have significantly more positive words compared to the proposed model.
基金supported by the National Science Foundation[grant numbers 1854502 and 1855902]Publication was made possible in part by support from the HKU Libraries Open Access Author Fund sponsored by the HKU Libraries.USDA is an equal opportunity provider and employer.Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S.Department of Agriculture.
文摘Morphological(e.g.shape,size,and height)and function(e.g.working,living,and shopping)information of buildings is highly needed for urban planning and management as well as other applications such as city-scale building energy use modeling.Due to the limited availability of socio-economic geospatial data,it is more challenging to map building functions than building morphological information,especially over large areas.In this study,we proposed an integrated framework to map building functions in 50 U.S.cities by integrating multi-source web-based geospatial data.First,a web crawler was developed to extract Points of Interest(POIs)from Tripadvisor.com,and a map crawler was developed to extract POIs and land use parcels from Google Maps.Second,an unsupervised machine learning algorithm named OneClassSVM was used to identify residential buildings based on landscape features derived from Microsoft building footprints.Third,the type ratio of POIs and the area ratio of land use parcels were used to identify six non-residential functions(i.e.hospital,hotel,school,shop,restaurant,and office).The accuracy assessment indicates that the proposed framework performed well,with an average overall accuracy of 94%and a kappa coefficient of 0.63.With the worldwide coverage of Google Maps and Tripadvisor.com,the proposed framework is transferable to other cities over the world.The data products generated from this study are of great use for quantitative city-scale urban studies,such as building energy use modeling at the single building level over large areas.
基金This work was supported by the WARMEST Program for Research and Innovation Horizon 2020 Marie Curie Research and Innovation Staff Mobility Project.RISE-2017 was carried out under the auspices of Research Groups ADEMAR,RNM 0179 and HUM 629 of the Junta de Andalucía and UCE-PP2018-01 of University of Granada.WARMEST MSC-RISE-H2020 project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement # 777981。
文摘This study proposes a methodology to detect weaknesses and strengths of a heritage destination.This methodology is based on the analysis of comments and opinions published by visitors on travel blogs and TripAdvisor.For the content analysis,the NVivo software has been used.The content analysis allows the identification of key aspects of the experience of tourists.The semantic network graphically shows the strengths and weaknesses.The research was carried out at two different time points,which have allowed to show,on the one hand,the relevance that the state of conservation of heritage destination has in the tourist experience and,on the other hand,that the proposed methodology helps managers of the heritage destination to improve the cultural tourist’s experience with the destination.
文摘阅读理解+阅读七选五(一)阅读理解A Gardens by the Bay Gardens by the Bay is the most popular attraction on TripAdvisor and it isn't hard to understand why.This place is mind-blowing and unlike anything we've ever seen.If you have time for just one attraction in Singapore,then this should be it.You can buy tickets at the gate but you can get a discount if you buy them in advance through Klook or Get Your Guide.
