Sentiment Analysis in Twitter: A Case Study in the Indian Airline Industry

Sentiment analysis is a technique of mining data for its sentiment content. It is one of the areas of text mining and natural language processing whose importance and scope is increasing day by day. Sentiment analysis helps us to extract sentiment of consumers from data. The data can be in any form. In this paper, we have used tweets as our data set. A large amount of data are generated everyday in social media platforms like Facebook, Twitter and others due to increased use of social media. Such data can be in the form of some status update or tweets. These data often reflect the sentiment of the users. Thus, we can harvest this large amount of data to extract sentiment of the consumers of any industry. The airline industry is rapidly growing with the number of passengers increasing day by day. The passengers often put forward their feedback or experience with a particular flight company on social media platform Twitter. Twitter has become a platform to present such reviews from the consumers of the airline industry. In this paper, we mine tweets from Twitter to present a case study of the Indian airline industry. An in-depth study of the sentiments has been presented. We have analysed the emotion content of the tweets of the airline passengers. The percentage of positive tweets was found highest for Vistara Airlines at 60.8 per cent. We use the Syuzhet (Extract Sentiment and Plot Arcs from Text, Matthew L. Jockers, 2015. https://github.com/mjockers/syuzhet) package available in R. We consider the major airline companies in India, namely IndiGo, Jet Airways, Air India, GoAir, SpiceJet, AirAsia and Vistara.