Associative Regressive Decision Rule Mining for Predicting Customer Satisfactory Patterns

Opinion mining also known as sentiment analysis, involves customer satisfactory patterns, sentiments and attitudes toward entities, products, services and their attributes. With the rapid development in the field of Internet, potential customer’s provides a satisfactory level of product/service reviews. The high volume of customer reviews were developed for product/review through taxonomy-aware processing but, it was difficult to identify the best reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is developed to predict the pattern for service provider and to improve customer satisfaction based on the review comments. Associative Regression based Decision Rule Mining performs twosteps for improving the customer satisfactory level. Initially, the Machine Learning Bayes Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After that, Regressive factor of the opinion words and Class labels were checked for Association between the words by using various probabilistic rules. Based on the probabilistic rules, the opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of service preferred by the customers with their review comments. The Associative Regressive Decision Rule helps the service provider to take decision on improving the customer satisfactory level. The experimental results reveal that the Associative Regression Decision Rule Mining (ARDRM) technique improved the performance in terms of true positive rate, Associative Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of similar pattern.