Sentiment Analysis on Airline Service Reviews using Data Mining based Classification Techniques

Abstract

Sentimental analysis is the field where online reviews, opinions, and sentiments from users are available and provide considerable amounts of information about the services, facilities, and status of the service provider in the market. In addition to examining the classification accuracy of standard data mining methods, this research evaluates the sentiments expressed about six social media microblog traveller networking site datasets relevant to Indian airlines. Using standard data mining classifiers, Bayes Net and SVM performed with high accuracy rates. In this paper main analysis of the classification performance of passenger sentiments for six airline services has been performed. However, we found that the Bayes Net performed best accuracy rate using WEKA tool but in case of using Rapid Miner tool SVM has produced the highest accuracy rate for our research among the other common standard classifiers. On the basis of thoroughly favourable service reviews from passengers, Go Air is consistently the airline that is most highly recommended.