Gaining customer knowledge in low cost airlines through text mining

Abstract

Purpose – The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines. Design/methodology/approach – About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion. Findings – The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and delays and post-booking management. Practical implications – Gaining knowledge of customer sentiments as well as improvements on the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight cancellations or delays and post-booking management will help LCCs to attract more customers and generate more profits. Originality/value – This paper provides useful insights on customers’ sentiments and opinions towards LCCs by utilizing social media information.