https://socictopen.socict.org/files/original/560b2f924377ef4452db162b91e262d8.pdf 7f33d75771dbb35f01cf3242ce5b8053 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry Creator An entity primarily responsible for making the resource Wen-Kuo Chen, Dalianus Riantama, Long-Sheng Chen Description An account of the resource Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining the knowledge of customer satisfaction is one of the critical tasks. Moreover, text reviews in social media have become one of important reference sources for customers’ decisions in buying services and products. Therefore, the main purpose of this study is to explore whether customer voices from social media reviews are different during the COVID-19 outbreak and to propose a new method to reduce interpersonal contact when collecting data. A text mining scheme which includes least absolute shrinkage and selection operator (LASSO) and decision trees (DT) are presented to discover the essential factors for customers to increase their satisfaction from unstructured online customer reviews. Finally, three real world review sets were employed to validate the effectiveness of the presented text mining scheme. Experimental results can help companies to properly adapt to similar epidemic situations in the future and facilitate their sustainable development. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource text mining, Feature selection, customer satisfaction, online customer reviews Identifier An unambiguous reference to the resource within a given context 10.3390/su13010268 Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental effects of industries and plants, Renewable energy sources, Environmental sciences