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• W3136827790 abstract "Purpose This study aims to conduct a “real-time” investigation with user-generated content on Twitter to reveal industry challenges and business responses to the coronavirus (Covid-19) pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded. Design/methodology/approach With 94,340 tweets collected between October 2019 and May 2020 by a programmed Web scraper, unsupervised machine learning approaches such as structural topic modelling are applied. Originality/value This study contributes to the literature on business response during crises providing for the first time a study of using unstructured content on social media for industry-level analysis in the hospitality context." @default.
• W3136827790 created "2021-03-29" @default.
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• W3136827790 date "2021-03-15" @default.
• W3136827790 modified "2023-09-24" @default.
• W3136827790 title "Revealing industry challenge and business response to Covid-19: a text mining approach" @default.
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• W3136827790 doi "https://doi.org/10.1108/ijchm-08-2020-0920" @default.
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