The Analysis and Prediction of Customer Review Rating Using Opinion Mining
Abstract- The customer review is important to improve service for company, which have both close opinion and open opinion. The open opinion means the comment as text which shows emotion and comment directly from customer. However, the company has many contents or group to evaluation themselves by rating and total rating for a type of services which there are many customer who needs to review. The problem is some customers given rating contrast with their comments. The other reviewers must read many comments and comprehensive the comments that are different from the rating. Therefore, this paper proposes the analysis and prediction rating from customer reviews who commented as open opinion using probability's classifier model. The classifier models are used case study of customer review's hotel in open comments for training data to classify comments as positive or negative called opinion mining. In addition, this classifier model has calculated probability that shows value of trend to give the rating using naive bayes techniques, which gives correctly classifier to 94.37°~ compared with decision tree Techniques.
Abstract- The customer review is important to improve service for company, which have both close opinion and open opinion. The open opinion means the comment as text which shows emotion and comment directly from customer. However, the company has many contents or group to evaluation themselves by rating and total rating for a type of services which there are many customer who needs to review. The problem is some customers given rating contrast with their comments. The other reviewers must read many comments and comprehensive the comments that are different from the rating. Therefore, this paper proposes the analysis and prediction rating from customer reviews who commented as open opinion using probability's classifier model. The classifier models are used case study of customer review's hotel in open comments for training data to classify comments as positive or negative called opinion mining. In addition, this classifier model has calculated probability that shows value of trend to give the rating using naive bayes techniques, which gives correctly classifier to 94.37°~ compared with decision tree Techniques.
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