Sentiment Analysis in Airline Data: Customer Rating Based Recommendation Prediction Using WEKA

Customer all over the world gives the ratings and reviews for the services they are using. Internet web sites (IWS) are a good platform for the customer to share their views regarding the offered services. These reviews and ratings are very useful for both customer and service provider, business-related purposes as it increases sales and is also useful for customers as it acts as a source of information. IWS makes it easy to share and access the data, but the presence of a huge amount of data makes it difficult to analyze, so the machine learning (ML) technique is developed to analysis, prediction, and recommendations. In this chapter, we have collected ratings on air transport management given by customers from different sites. There are ratings on seat comfort, cabin staff, food beverage, inflight entertainment and many more, which is further combined to give the overall rating through which recommendation is done. We have used different ML techniques to find out the overall sentiments generated by the customers on different service aspects and give the most suitable recommendation to customers. This helps customers as in travelers in decision making based on service type. We have basically compared Random tree and Decision tree ML techniques for recommendation prediction. In this chapter, we have been used WEKA as a tool to apply these ML techniques. Finally, the accuracy of the result is calculated using precision, recall, and F-scores.