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Exploring cider website descriptions using a novel text mining approach

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dc.contributor.authorCalvert, Martha D.en
dc.contributor.authorCole, Elizabethen
dc.contributor.authorNeill, Clinton L.en
dc.contributor.authorStewart, Amanda C.en
dc.contributor.authorWhitehead, Susan R.en
dc.contributor.authorLahne, Jacoben
dc.date.accessioned2023-06-23T14:00:26Zen
dc.date.available2023-06-23T14:00:26Zen
dc.date.issued2023-05en
dc.description.abstractRapid methods of text analysis are increasingly important tools for efficiently extracting and understanding communication within the food and beverage space. This study aimed to use frequency-based text mining and biterm topic modeling (BTM) as tools for analyzing how cider products are communicated and marketed on cider-producer websites for products made in Virginia, Vermont, and New York. BTM has been previously used to explore topics in small corpora of text data, and frequency-based text mining is efficient for exploring patterns of text across different documents or filters. The present dataset comprised 1115 cider products and their website descriptions extracted from 124 total cider-producer websites during 2020 and 2021. Results of the text mining analyses suggest that cider website descriptions emphasize food-pairing, production, and sensory quality information. Altogether, this research presents the text mining approaches for exploring food and beverage communication. Practical applicationsThis research will be valuable to stakeholders in the United States' cider industry by providing relevant insight as to how cider marketing and sensory communication varies based on extrinsic product factors, such as geography and packaging. This research also demonstrates the efficiency and potential of text mining tools for exploring language and communication related to foods, beverages, and sensory quality. Further, this research provides a framework for extracting sensory-specific language from a large corpus of data, which may be adopted by other researchers wishing to apply rapid descriptive methods in the sensory, quality, and consumer research fields.en
dc.description.notesUSDA-NIFA AFRI, Grant/Award Number:2020-68006-31682en
dc.description.sponsorshipUSDA-NIFA AFRI [2020-68006-31682]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1111/joss.12854en
dc.identifier.eissn1745-459Xen
dc.identifier.issn0887-8250en
dc.identifier.urihttp://hdl.handle.net/10919/115496en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectconsumptionen
dc.subjectcraften
dc.subjectfooden
dc.subjectpreferencesen
dc.subjectexperienceen
dc.subjectattitudesen
dc.subjecttasteen
dc.subjectbeeren
dc.titleExploring cider website descriptions using a novel text mining approachen
dc.title.serialJournal of Sensory Studiesen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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