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Identifying unique features of the image of selected cities based on reviews by TripAdvisor portal users
Scandinavian Journal of Hospitality and Tourism ( IF 4.694 ) Pub Date : 2020-10-13 , DOI: 10.1080/15022250.2020.1833362
Marek Nowacki 1 , Agnieszka Niezgoda 2
Affiliation  

ABSTRACT

The paper aims to identify unique features in the image of four Baltic cities: Gdansk, Kaliningrad, Riga, and Szczecin, based on an analysis of reviews posted on the TripAdvisor portal. The text mining technique was used to extract the words most frequently used in opinions, while sentiment analysis was performed to assess the strength of negative and positive reviews. Analysis of variance was used to extract the unique and common features of the image of each city analysed. The results showed that Riga and Gdansk have the largest number of unique features/attributes, while Kaliningrad has the smallest. Positive and negative sentiment analysis indicated that Gdansk and Szczecin have a higher proportion of positive sentiment in reviews than Riga and Kaliningrad. The study confirmed the importance of traveller-generated content as an image-building agent, and shows that destination image attributes can be effectively identified using text mining in both the cognitive and affective dimensions. It also showed that it is possible to identify significant differences in the image of a destination, which can subsequently be used by DMOs in the branding process to distinguish destinations from one another on the tourism market.



中文翻译:

根据TripAdvisor门户网站用户的评论识别选定城市形象的独特特征

摘要

本文旨在根据TripAdvisor门户网站上发布的评论分析,找出波罗的海四个城市(格但斯克,加里宁格勒,里加和什切青)的图像中的独特特征。文本挖掘技术用于提取意见中最常用的单词,而情感分析则用于评估负面和正面评论的强度。方差分析用于提取所分析的每个城市的图像的独特特征和共同特征。结果表明,里加和格但斯克的独特功能/属性最多,而加里宁格勒的独特功能/属性最少。正面和负面情绪分析表明,格但斯克和什切青的正面情绪比例高于里加和加里宁格勒。这项研究证实了旅行者生成的内容作为图像构建媒介的重要性,并表明可以使用文本挖掘在认知和情感维度上有效地识别目的地图像属性。它还表明,可以识别出目的地图像中的显着差异,随后DMO可以在品牌塑造过程中使用这些差异来区分旅游市场上的目的地。

更新日期:2020-10-13
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