Improved Customer Churn Prediction Model Using Word Order Contextualised Semantics on Customers’ Social Opinion

Ayodeji Ibitoye, Olufade Onifade


Through the hype in digital marketing and the continuous increase in volume and velocity of opinion about organisation’s brands, churn prediction now requires advanced analytics in opinion mining for effective customer behavioural management beyond keywords sentiment analysis (SA). Earlier, by analysing customers’ opinion using SA models, the extracted positive-negative polarity is used to classify customers as churner, or non-churner. In those methods, the impact of word order, context and the inherent semantics of the clustered opinion set were oftentimes overlooked. However, with the consistent creation of new words with new meanings mapped to existing words on the Web, the research extended Fuzzy Support Vector Model (FSVM) to show that the dependency distance between the headword, it dependent and tail word can be weighted by using information content derived from a corpus to generate four-classed social opinion categories as Strong positive, Positive, Negative and Strong Negative. This opinion classes, formed the basis for the churn category as Premium customer, Inertia customer Potential Churner, and Churner in customer behavioral management. In performance evaluation, aside engendering quadrupled churn class against the existing churn binary pattern, a better accuracy, precision and recall values were obtained when compared with existing SA works in Support Vector Machine and FSVM, respectively.



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International Journal of Advances in Applied Sciences (IJAAS)
p-ISSN 2252-8814, e-ISSN 2722-2594

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