Sentiment Analysis of Airport Customer Reviews

Abstract

Customer satisfaction plays an important factor for the business’ success, particularly in aviation industries. One way to measure customer satisfaction level is using customer reviews. This study evaluates and analyzes customer reviews of services and facilities of Soekarno-Hatta Airport as the largest airport in Indonesia using text mining approach of sentimental analysis. Support vector machine and Naïve Bayes classifier are classification techniques used to identify positive or negative sentiments contained in review sentence. The results of classification techniques for sentiment analysis in this study indicate that support vector machine has higher accuracy value than Naïve Bayes Classifier in analyzing sentiments. The output of this study is evaluation in improving the quality of airport services and facilities, identification of service aspect and airport facility which become the strength and weakness as well as improvement prioritization of aspects that still become weakness in achieving desired level of customer satisfaction.