Linking emotion and place on Twitter at Disneyland

Park, Seunghyun Brian; Kim, Hyung Jin; Ok, Chihyung

doi:10.1080/10548408.2017.1401508

Cited by 28 publications

(22 citation statements)

References 80 publications

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“…For this study, we selected Twitter as the social media platform because it is widely adopted by users (Mora-Cantallops and Sá nchez-Alonso, 2019), and it provides a valid and publicly available extraction tool to access data easier than the other social networks like Facebook. The information this medium provides about tourist experiences is helpful for tourism operators, destination marketers, and researchers (Park et al, 2018). In 2018, Twitter had around 334 million active users per month (Abeysinghe et al, 2018) who were producing significant content daily through tweets.…”

Section: Methods Of Data Collectionmentioning

confidence: 99%

“…In particular, social media is sought as a desirable choice for crisis-related information and is perceived as offering a more trusted insider's view of the situation over traditional media coverage (Schroeder and Pennington-Gray, 2015). Further, positive emotional experiences, like feeling happiness or delight, have been linked to positive consumer outcomes, such as, customer satisfaction and future word-of-mouth, in social media (Park et al, 2018). On a larger scale, people's opinion can have a reciprocal relationship with government decisions (Abeysinghe et al, 2018), and this is relevant given the policy choices made by governments to deal with the pandemic, for example, COVID-19 lockdown and closed borders.…”

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

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Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Nadeau

Wardley

Rajabi

2021

Tourism and Hospitality Research

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The COVID-19 pandemic is having a significant impact on tourism, and emotion projection is one way to understand the extent of destination image resiliency during the crisis. Therefore, this research captured emotions expressed in social media during a peak pandemic month to compare to the prior year period. Toronto and New York were selected due to their tourism importance within their countries but to also compare the effects of different policy approaches used during the pandemic. This study found resiliency of the destination images although there was a significant increase in projections of fear for both cities. Additionally, there was a significant divergence observed for the two cities with a decrease in joy and an increase in sadness projections for New York versus Toronto. This implies that tourism destination marketers have a stable basis of emotions to use in communications, but there are weaknesses to address.

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Section: Methods Of Data Collectionmentioning

confidence: 99%

Section: Emotions and Tourism Destination Imagesmentioning

confidence: 99%

Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Nadeau

Wardley

Rajabi

2021

Tourism and Hospitality Research

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“…In the field of marketing, sentiment analysis is widely used to associate customers' emotions with customer satisfaction and potential future sales. Researchers (Park et al, 2018) have conducted research to analyze major topics, tourists' emotions and hot spots in Disneyland with social media analytics and geographic information system analysis. Studies like this one propose new frameworks for marketers to understand their customers better.…”

Section: Quantification Of Emotions In the Private Sectormentioning

confidence: 99%

A Sentiment Analysis Model of a Civil Service Performance Evaluation Using a Feminist Framework

Cao¹

2021

Preprint

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The aim of this article is to explore using sentiment analysis to assess the openness ofgovernments and establish a dynamic evaluation mechanism to supervise governments, andconsequently improve the administrative law on a feminist basis. I build a sentiment analysismodel based on communication between me and governments, discuss the implication of themodel, and propose potential improvements to the administrative law in China.

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“…Seven studies belonging to this cluster (Hawkins et al, 2016;Kim et al, 2016;D'avanzo, 2017;Rogers et al, 2017;Walker et al, 2017;Adamopoulos et al, 2018;Park et al, 2018) conducted sentiment analysis and NLP by analyzing consumer tweets. Hawkins et al (2016) employed over 400.000 patient tweets and conducted sentiment analysis with a Phyton library named "TextBlob" for evaluating Twitter usage as a data stream aiming to measure perceived quality of US hospitals, and found that tweet sentiment is not associated with established healthcare quality ratings.…”

Section: Svm For Purchase Intention;mentioning

confidence: 99%

Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

Dobrucalı¹,

Ilter²

2020

Journal of Yaşar University

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Social media analytics (SMA), referring to the collection and analysis of user generated data from social media platforms, attract attention of both researchers and practitioners striving to derive consumer insights. The SMA domain grows multifariously, with a highlight on the capability of machine learning algorithms in capturing noteworthy insights through processing high-volume and complex data in a cost effective way. As machine learning applications draw attention as a fertile area that may re-shape the future of SMA, there is a need to comprehend trends and approaches in an integrative framework. Accordingly, this study aims to present an integrative framework by portraying machine learning application trends and approaches in SMA. 42 scientific articles published in refereed scientific business, management, and computational science journals between the years 2013 and 2019 are analyzed via systematic literature review based on visual text mining method (SLR-VTM). The results revealed five distinctive research clusters as: (1) review sites, (2) microblogs, (3) social networking sites, (4) content communities, (5) cross-media. This analysis plays a crucial role for enhancing our understanding regarding the intellectual structure of the field, acknowledging the leading studies of the domain, better positioning future research, and determining gaps and new paths for researchers.

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Linking emotion and place on Twitter at Disneyland

Cited by 28 publications

References 80 publications

Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

Tourism destination image resiliency during a pandemic as portrayed through emotions on Twitter

A Sentiment Analysis Model of a Civil Service Performance Evaluation Using a Feminist Framework

Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

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