Feature selection using binary particle swarm optimization algorithm to predict repurchase intention from customer reviews


  • Dimas Adrianto
  • Dedy Suryadi Department of Industrial Engineering, Universitas Katolik Parahyangan, Bandung, Indonesia.




Binary Particle Swarm Optimization, Feature selection, online customer reviews, repurchase intention


Indonesia has the largest digital economy in Southeast Asia and has a promising market for e-commerce companies to compete and dominate the online market share. This also gave rise to an increment in the number of customer reviews of a product or service provided. Online customer reviews can be utilized to analyze repurchase intention of e-commerce customers. However, a large number of features that appears in customer reviews increased the repurchase intention predictive model complexity. A process to choose a subset of features and reduces the number of features in data is called feature selection. This paper proposed a method on feature selection to pre-process the predictive model by reducing features that appears in the model using Binary Particle Swarm Optimization combined with Sentiment Orientation-Pointwise Mutual Information to reduce the complexity. The results shows that the proposed method reduces and selects the best features to construct a predictive model of repurchase intention from online customer reviews on two datasets that are written both in Indonesian and English. Compared to the baseline model before performing feature selection, the accuracy of the predictive models evaluated using k-Nearest Neighbors on both datasets are increased 5.4% and 8.5%, respectively.