{"title":"Verbal Construction of Destination Image through Tourists’ Word of Mouth with Guilin as a Reference for Destinations in China and around the World","authors":"Jianbin Zhu, Jiangshan Xia, Shuanghua Zhang","doi":"10.1163/15692108-12341520","DOIUrl":null,"url":null,"abstract":"\n The discursive construction of strong and favorable destination image has been a recent topic of discussion in both discourse and tourism studies. Aiming at unpacking what image has been constructed about Guilin as a typical scenery destination in China, this study conducts a corpus based quantitative discourse analysis on tourists’ word of mouth data about Guilin. Specifically, drawing on the Latent Dirichlet Allocation model, this study conducts topic-based sentiment analyses on topics in online travelers’ review data to detect their sentiments and attitudes. Results indicate that international tourists, on the whole regard Guilin as an attractive destination in China with beautiful scenery, exciting river cruise and many other attractions, but improvements are still needed to boost the cost performance of traveling in Guilin. These findings are expected to provide useful insights for the development of tourist attractions in Guilin and in other similar scenery attractions around Asian and Africa as well, the promotion and marketing of their images as an international tourist destination, as well as the improvement of managerial practice. The methods proposed in the current study can also be used to analyze discursive construction of image in language big data in future studies.","PeriodicalId":54087,"journal":{"name":"African and Asian Studies","volume":" ","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2022-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"African and Asian Studies","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1163/15692108-12341520","RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AREA STUDIES","Score":null,"Total":0}
引用次数: 1
Abstract
The discursive construction of strong and favorable destination image has been a recent topic of discussion in both discourse and tourism studies. Aiming at unpacking what image has been constructed about Guilin as a typical scenery destination in China, this study conducts a corpus based quantitative discourse analysis on tourists’ word of mouth data about Guilin. Specifically, drawing on the Latent Dirichlet Allocation model, this study conducts topic-based sentiment analyses on topics in online travelers’ review data to detect their sentiments and attitudes. Results indicate that international tourists, on the whole regard Guilin as an attractive destination in China with beautiful scenery, exciting river cruise and many other attractions, but improvements are still needed to boost the cost performance of traveling in Guilin. These findings are expected to provide useful insights for the development of tourist attractions in Guilin and in other similar scenery attractions around Asian and Africa as well, the promotion and marketing of their images as an international tourist destination, as well as the improvement of managerial practice. The methods proposed in the current study can also be used to analyze discursive construction of image in language big data in future studies.
期刊介绍:
The journal presents a scholarly account of studies of individuals and societies in Africa and Asia. Its scope is to publish original research by social scientists in the area of anthropology, sociology, history, political science and related social sciences about African and Asian societies and cultures and their relationships. The journal focuses on problems and possibilities, past and future. Where possible, comparisons are made between countries and continents. Articles should be based on original research and can be co-authored.