Summarizing customer review based on product feature and opinion

Opinion or sentiment analysis has risen to extract useful information from a lot of unstructured text data, in the form of customer reviews on different products and their features or online SNS data respectively. Customer reviews are not only helpful for potential customers, but also are helpful for the manufacturers of the products to raise their products and services. The reviews conciseness takes the attention of the customers rather than long reviews. Opinion Mining is playing a major role to summarize customer reviews and make it easy for online customers to determine whether to purchase the products or not. In this paper, we propose a supervised lazy learning model utilizing syntactic rules for the product features and opinion words extraction in subjective review sentences. In our lazy learning algorithm, i.e. K-NN with k=3 is used for the review sentences' classification into two classes (subjective, objective). Our experiment shows that proposed method can improve the performance of existing work in terms of average precision, recall and f-score for the extraction of opinion sentences and product features.