Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics

Park, Seunghyun Brian; Kim, Jinwon; Lee, Yong Kyu; Ok, Chihyung

doi:10.1016/j.tourman.2020.104127

Cited by 68 publications

(48 citation statements)

References 88 publications

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“…To achieve the research objectives, text mining was chosen as a research method as it assists in identifying meaning from the vast textual information and establishes trends and patterns about a specific topic (Park et al, 2020;Roy et al, 2020). Initially, we have selected 15 video blog posts of the reputed travel bloggers who have shared their one-day tour experience in Digha during the COVID-19 restriction.…”

Section: Methodsmentioning

confidence: 99%

Analyzing one-day tour trends during COVID-19 disruption – applying push and pull theory and text mining approach

Roy

Sharma

2020

Tourism Recreation Research

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Fear of COVID-19 and the quarantine lifestyle make travellers look for new ways of travelling to get rid of boredom and anxiety. People are looking for one-day tours to break the monotony. The study presents the travel motivations of people during the pandemic based on the Push and Pull theory. The study's objective is to examine the relationship between push and pull motives for initiating one-day tours when mobility is partially restricted. It examines the moderating effect of COVID-19 related factors on the push and pull motives. The text mining approach and NLP is used to analyze Vlog review data to capture sentiments and emotions. The study gives insight into the traveller's emotional state in the context of one-day tours during these critical times. The text mining reveals the push and pull motives for one day tours and the negative moderating influence of concern for safety and fear of infection. The findings indicate that travellers prefer "day use hotel", "clean beaches", and "home picnic" environment with "proper hygiene" at the tourist locations. These findings give insight into the consumers' travel motivations and concerns, thereby providing service providers with cues to tailor their offerings to stimulate one day tours.

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Section: Methodsmentioning

confidence: 99%

Analyzing one-day tour trends during COVID-19 disruption – applying push and pull theory and text mining approach

Roy

Sharma

2020

Tourism Recreation Research

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“…With the remarkable growth of technology in the era of Web 2.0, the internet, big data and related technologies have brought forth new data sources and caused a paradigm shift in scientific research, including in the field of hospitality and tourism (Li et al , 2018; Mariné-Roig, 2019; Park et al , 2020; Zhang et al , 2020). SMA, as a subset of big data analytics, uses any form of content available via social media platforms such as blogs, discussion forums, posts, chats, tweets, podcasting, pins, digital images, video, audio files, or others (Choi et al , 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, social media data tend to be massive and complex. In terms of data quality, the large scale of social media data could effectively mitigate sample size limitations and sampling bias issues (Kirilenko et al , 2021; Park et al , 2020). Social media data is also more informative and complex, thus identifying the underlying behavior patterns that can offer meaningful insights (Kirilenko et al , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…They recommended that researchers use UGC to supplement findings from traditional survey-based research methods and gain additional insights. Compared to traditional surveys and focus group methods with small- and fixed-scale data sets (Li et al , 2017), social media analytics (SMA), as part of big data analytics, collects and analyzes unstructured and complex data with unmatched breadth, depth and scale (Du et al , 2018; Park et al , 2020; Zhang et al , 2020). Additionally, SMA can be used to solve real-life hospitality and tourism industry issues in near real-time.…”

Section: Introductionmentioning

confidence: 99%

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Destination image through social media analytics and survey method

Lin

Liang

Xue

et al. 2021

IJCHM

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Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

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“…Häberle et al [11] confirmed that the spatial distribution of Twitter content can be studied through various machine-learning techniques, and Gulnerman and Karaman [12] presented various techniques for mapping social-media data. Recent advances in these techniques have expanded their applications from comparing the location of McDonald's outlets to obesity incidence by spatializing Twitter data [13], to proposing methods for maximizing joy via an emotional analysis of space in a Disney park [14]. The present study maps environmental conflicts in South Korea, which has experienced various environmental conflicts due to rapid development in recent years, using spatial text mining and, where possible, online environmental conflict data provided by local governments.…”

Section: Introductionmentioning

confidence: 99%

Mapping Environmental Conflicts Using Spatial Text Mining

Lee

Kim

2020

Land

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Mapping the characteristics and extent of environmental conflicts related to land use is important for developing regionally specific policies. However, because it is only possible to verify the frequency of conflicts on a specific predetermined subject, it is difficult to determine the various reasons for conflicts in a region. Therefore, this study mapped the current status of regional environmental conflicts in South Korea using a spatial text mining technique, then proposed relevant management policies. The results were obtained by analyzing environmental conflict data extracted from the online agendas of regional environmental organizations. Air quality-related conflicts in South Korea are concentrated in western municipalities; development-related conflicts are concentrated in the southern region of Jeju Island; and intensive safety-related conflicts occur in metropolitan areas, particularly Ulsan. Thus, the type of conflict is determined by the local environment, in accordance with the definition of environmental conflict, and the distribution is determined by the location of the stakeholder population. This study reveals the issues and locations related to local environmental conflict that require further attention, and proposes more wide-ranging methods for managing the links between conflicts by mapping environmental conflicts on a large scale rather than on an individual basis.

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Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics

Cited by 68 publications

References 88 publications

Analyzing one-day tour trends during COVID-19 disruption – applying push and pull theory and text mining approach

Analyzing one-day tour trends during COVID-19 disruption – applying push and pull theory and text mining approach

Destination image through social media analytics and survey method

Mapping Environmental Conflicts Using Spatial Text Mining

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