Quality Assessment of Customer Reviews Extracted from Web Pages : a Review Clustering Approach

The number of customer reviews that a product receives is growing at very fast rate. Customer reviews posted on the websites vary greatly in quality. In this paper, we make an attempt to assess a review based on its quality, to help the customer make a proper buying decision. The quality of customer review is assessed as most significant, more significant, significant and insignificant. A novel and effective web mining technique based on review clustering is proposed for assessing a customer review of a particular product. This is performed in two steps : (1) Cluster the reviews into four groups by applying k-means clustering technique and compute the cluster weights. (2) Assess quality of the given reviews and classify them by considering the cluster weights. Experimentation has been done using publicly available review databases for four different products. The results are analyzed and the efficacy of the proposed method has been demonstrated.