Customer Churn Prediction Based on Sentiment Score - Al-Safi, Shadha (AUC Knowledge Fountain, 2024)
In recent years, the telecommunications industry has witnessed intensified competition, wherein the expense associated with acquiring new consumers exceeds that of sustaining existing ones. Consequently, predicting customer churn prior to its occurrence has become essential. This study proposes a sentiment-based customer churn prediction model in which the sentiment of customers is predicted using Random Forest. Subsequently, the derived sentiment predictions are combined with additional features to predict customer churn. The ensemble technique is applied to predict churn, consisting of K-nearest neighbors, Support Vector Machines, Random Forest as base learners, and Multiple Layer Perceptron as a meta learner. Moreover, mutual information is applied to select the pertinent features impacting customer churn, and the class imbalance is handled through the utilization of the class weighted technique. The results of the experiments reveal that the proposed model surpassed the state-of-the-art customer churn models as it achieved an accuracy of 98.86%, an AUC of 99.47 %, and an F1-score of 97.77%.