A machine learning approach to analyze customer satisfaction from airline tweets

Abstract

IntroductionAdvancement in technology has boosted the availability and use of smart mobile phones. At present, the number of smart phone users is 2.71 billion across the world [1]. The major online social media (SM) platforms i.e. Facebook, Twitter and Instagram are available as mobile applications in the smart phones. Therefore, there is no need to visit cyber cafes to access them, as everything is available in the smart phones.Every piece of information shared on SM carries an emotion, sentiment or feeling. These emotions can be positive, negative and neutral. All these emotions may come from a travel trip, restaurant trip, exhibitions, movies, elections, hospital visits etc. These emotions carries some hidden information related to comfort/discomfort in related areas. Hence, there is a good scope of analyzing this information to detect the patterns of the emotions. This analysis can help us to understand the emotions of the people in respective domain and the reasons behind it.Air travel is one of the most convenient mode for long distance travel at both national and international level [2]. There are many airline service providers (ASPs) around the world. The competitive world motivates the airlines company to attract the customers. AbstractCustomer's experience is one of the important concern for airline industries. Twitter is one of the popular social media platform where flight travelers share their feedbacks in the form of tweets. This study presents a machine learning approach to analyze the tweets to improve the customer's experience. Features were extracted from the tweets using word embedding with Glove dictionary approach and n-gram approach. Further, SVM (support vector machine) and several ANN (artificial neural network) architectures were considered to develop classification model that maps the tweet into positive and negative category. Additionally, convolutional neural network (CNN) were developed to classify the tweets and the results were compared with the most accurate model among SVM and several ANN architectures. It was found that CNN outperformed SVM and ANN models. In the end, association rule mining have been performed on different categories of tweets to map the relationship with sentiment categories. The results show that interesting associations were identified that certainly helps the airline industries to improve their customer's experience. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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