TY - JOUR
T1 - Machine learning approach to auto-tagging online content for content marketing efficiency
T2 - A comparative analysis between methods and content type
AU - Salminen, Joni
AU - Yoganathan, Vignesh
AU - Corporan, Juan
AU - Jansen, Bernard J.
AU - Jung, Soon Gyo
N1 - Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/8
Y1 - 2019/8
N2 - As 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.
AB - As 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.
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U2 - 10.1016/j.jbusres.2019.04.018
DO - 10.1016/j.jbusres.2019.04.018
M3 - Article
AN - SCOPUS:85064625684
SN - 0148-2963
VL - 101
SP - 203
EP - 217
JO - Journal of Business Research
JF - Journal of Business Research
ER -