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

Volume: 101, Pages: 203 - 217
Published: Aug 1, 2019
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...
Paper Details
Title
Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type
Published Date
Aug 1, 2019
Volume
101
Pages
203 - 217
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