Show simple item record

dc.contributor.authorSalminen, J.*
dc.contributor.authorYoganathan, Vignesh*
dc.contributor.authorCorporan, J.*
dc.contributor.authorJansen, B.J.*
dc.contributor.authorJung, S.-G.*
dc.date.accessioned2019-05-28T10:00:31Z
dc.date.available2019-05-28T10:00:31Z
dc.date.issued2019-08
dc.identifier.citationSalminen J, Yoganathan V, Corporan J et al (2019) Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research. 101: 203-217.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17058
dc.descriptionYesen_US
dc.description.abstractAs complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.jbusres.2019.04.018en_US
dc.rights© 2019 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.subjectMachine learningen_US
dc.subjectAuto-taggingen_US
dc.subjectWeb contenten_US
dc.subjectContent marketingen_US
dc.subjectNeural networken_US
dc.subjectDigital marketingen_US
dc.titleMachine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content typeen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-04-12
dc.date.application2019-04-28
dc.typeArticleen_US
dc.date.EndofEmbargo2020-12-29
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo on 29 Dec 2020.en_US


Item file(s)

Thumbnail
Name:
salminen_et_al_2019.pdf
Embargo:
2020-12-29
Size:
584.9Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record