Airline Sentiment Visualization, Consumer Loyalty Measurement and Prediction using Twitter Data

Abstract

Social media today is an integral part of people's daily routines and the livelihood of some. As a result, it is abundant in user opinions. The analysis of brand specific opinions can inform companies on the level of satisfaction within consumers. This research focus is on analysis of tweets related to airlines based in four regions: Europe, India, Australia and America for consumer loyalty prediction. Sentiment Analysis is carried out using TextBlob analyzer. The tweets are used to calculate and graphically represent the positive, negative mean sentiment scores and a varying mean sentiment score over time for each airline. The terms with complaints and compliments are depicted using visualization methods. A novel method is proposed to measure consumer loyalty using the data gathered from Twitter. Furthermore, consumer loyalty prediction is performed using Twitter data. Three classifiers are employed, namely, Random Forest, Decision Tree and Logistic Regression. A maximum classification accuracy of 99.05% is observed for Random Forest on 10-fold cross validation.