Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach

Abstract

Customers post online product reviews based on their own experience. They may share their thoughts and comments on items on online shopping websites. The sentiment analysis comprises of opinion or idea process and process of sorting high rating reviews according to how well the product satisfies. Opinion mining is a technique for extracting useful data from large amounts of texts in order to use those to enhance or expand a company's operations. According to consumer evaluations, many of the goods aren't as good as they seem. It's common that buyers submit their thoughts on a product but then forget to rate it. The prior data preprocessing is more efficient to extract the features by CNN approach. This proposed methodology breaks down each user's rating prediction model into two parts: one based on the review text and other based on the user rating matrix with the help of CNN feature engineering. The goal of this study is to classify all reviews into ratings by SVM model. This proposed classification model provides good accuracy to predict the online reviews efficiently. For reviews without ratings, a further prediction of feelings is generated using multiple classifiers. The benefits of this proposed model are honed using helpfulness ratings from a small number of evaluations such as accuracy, F1 score, sensitivity, and precision. According to studies using the standard benchmark dataset, the accuracy of customized recommendation services, user happiness, and corporate trust may all be enhanced by including review helpfulness information in the recommender system.