Tourism destination image based on tourism user generated content on internet

Abstract

Purpose The purpose of this paper is to study tourists’ spatial and psychological involvement reflected through tourism destination image (TDI), TDI is divided into on-site and after-trip groups and the two groups are compared in the frame of three-dimensional continuums. Design/methodology/approach By conducting latent Dirichlet allocation (LDA) modeling to tourism user-generated content, structural topic models are established. The topics separated out from unstructured raw texts are structural themes and representations of TDI. Social network analysis (SNA) reveals the quantitative and structural differences of three-dimensional continuums of the two TDI groups. Findings The findings reveal that from the stage of on-site to after-trip, tourist perception of TDI shifts from psychologically to functionally-oriented, from common to unique, and from holistic to more attribute focused. Also, it is suggested that from a postmodernism perspective, TDI is never unique, fixed or universal, but has different image perceptions and feedbacks for different tourists. Research limitations/implications With the assistance of social sensing, a panoramic view of TDI could be established. Targeted and precision destination marketing and image promotion could be applied out to each individual tourist. Originality/value Combining with the perspectives of the tourist-destination space system and the tourism involvement theory, this research proposes a TDI transformation model and an explanation of the internal mechanism. The originality of research also lies in the methodological innovation of social sensing data and the LDA topic model.

Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach

“…Hence, 5,000 posts were randomly selected for each attraction using a random selection in excel. The sample size is similar to recent studies applying topic modeling technique [41,42]. To ensure an equal distribution of the data, two attractions (i.e., Museum of Natural History and City of Science and Industry) having less than 300 reviews were excluded.…”

A web text mining approach for the evaluation of regional characteristics at the town level

“…The term frequency (TF) method and other derived models for different subjects (Ibrahim & Landa‐Silva, 2016; Lopes, Fernandes, & Vieira, 2012; Robertson, 2004) can be used to extract high‐frequency or important terms from the text of the region to describe the RCs (Bohannon, 2010; Michel et al., 2011). For example, a tourism destination image or a comprehensive description of the typical characteristics of a region may be obtained from tourism‐generated content or text on the internet (Wang et al., 2021; Wong & Qi, 2017). However, these typical characteristics are RCs, because quantitative comparisons of the RCs in different regions are required to determine if these characteristics are unique to the region.…”

“…The amount of information available on the Web has increased rapidly in the current internet era. The internet has become an information exchange platform that stores massive amounts of descriptive text about regions and relevant information (Perovšek, Kranjc, Erjavec, Cestnik, & Lavrač, 2016; Wang, Li, Wu, & Wang, 2021; Wang, Wang, Li, & Wu, 2015). For example, travel blogs describe the local food and scenery.…”

Reading between the lines: analyzing online reviews by using a multi-method Web-analytics approach

“…Social media platforms and travel websites across the world have witnessed a huge increase in user-generated content (UGC) over the past decade (Choi et al, 2018;Wang et al, 2020). These websites have enabled the users to freely express their feelings and views with fellow travelers or the public in general over a trip or tourist destination that they visit (Lee and Choi, 2020;Alaei et al, 2019;Tsujioka et al, 2020).…”

“…These websites have enabled the users to freely express their feelings and views with fellow travelers or the public in general over a trip or tourist destination that they visit (Lee and Choi, 2020;Alaei et al, 2019;Tsujioka et al, 2020). The significance of UGC in travelers' decision-making process has been empirically established (Zeng and Gerritsen, 2014;Wang et al, 2020). The influence of social websites is so much that people alter their travel plans based on the content posted on these websites (Mediabistro, 2012).…”