Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type

Joni Salminen, Vignesh Yoganathan, Juan Corporan, Bernard J. Jansen, Soon Gyo Jung

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)203-217
Number of pages15
JournalJournal of Business Research
Volume101
DOIs
StatePublished - Aug 2019

All Science Journal Classification (ASJC) codes

  • Marketing

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