出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Opinion mining also known as sentiment analysis, involves customer satisfactory patterns,sentiments and attitudes toward entities, products, services and their attributes. With the rapiddevelopment in the field of Internet, potential customer’s provides a satisfactory level ofproduct/service reviews. The high volume of customer reviews were developed forproduct/review through taxonomy-aware processing but, it was difficult to identify the bestreviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique isdeveloped to predict the pattern for service provider and to improve customer satisfaction basedon the review comments. Associative Regression based Decision Rule Mining performs twostepsfor improving the customer satisfactory level. Initially, the Machine Learning BayesSentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. Afterthat, Regressive factor of the opinion words and Class labels were checked for Associationbetween the words by using various probabilistic rules. Based on the probabilistic rules, theopinion and sentiments effect on customer reviews, are analyzed to arrive at specific set ofservice preferred by the customers with their review comments. The Associative RegressiveDecision Rule helps the service provider to take decision on improving the customer satisfactorylevel. The experimental results reveal that the Associative Regression Decision Rule Mining(ARDRM) technique improved the performance in terms of true positive rate, AssociativeRegression factor, Regressive Decision Rule Generation time and Review Detection Accuracy ofsimilar pattern.
