Sentiment Analysis of Twitter Data to Explore Customers’ Feedback Towards US Airline Services - Liu, Wanru (2020)
Among the competitive airline industries, understanding the customers' opinions and improving them accordingly is very important for improving customer satisfaction to retain old customers and attract new customers. This research aims to carry out sentiment analyses on Twitter data relevant to the US airlines to determine the drivers behind the sentiment of each customer review. The identification of the drivers allows appropriate recommendations to be suggested to each airline to improve their services and the utilisation of machine learning methods enables the generation of an accurate model that could predict and classify the sentiments of customer reviews.