A machine learning-based approach to enhancing social media marketing. (September 2020) - Arasu, B. Senthil; Seelan, B. Jonath Backia; Thamaraiselvan, N.
Highlights: Digital marketing strategy requires introduction of AI tools to transform the data flow into valuable customer insights and to streamline marketing processes with significant breakthrough in business growth. This research explores the possibility of finding efficient classification, clustering and prediction algorithms with the use of a machine learning tool called WEKA. The major parameters such as precision, recall, and F-measure justify the increased performance score of WEKA when compared with other approaches such as rapid Miner, Orange, Knime and R. Abstract: Social media (SM) represent beneficial channels for marketers, business promoters and consumers. To acquire continuous revenues and more active customers, key business players should understand the behaviour and purchase preferences of buyers. To predict the buying decisions of purchasers, data about purchase intentions and desires have to be extracted with the help of data mining techniques. The purpose of this paper is to examine social media data analytics using machine learning tools; this new approach for developing a social media marketing strategy employs the Waikato Environment for Knowledge Analysis (WEKA). WEKA is compared with other algorithms of interest and found to outperform its peers, especially with regard to parameters such as precision, recall, and F-measure, indicating that WEKA performs better than other approaches. Graphical abstract: Image, graphical abstract