Predicting passengers’ feedback rate for airport service quality

Abstract

Airport service quality evaluation is commonly found on social media sites, including Google Maps. The reviews by users of Google Maps are longer in terms of the number of words than those found on Twitter. They also include a rating, whereas those on Twitter need to be labelled. However, they are less well known than those on Twitter amongst researchers who focus on sentimental analysis. This study attempts to fill the gap in the current literature and develops architecture that is based on Long-Short Term Memory Neural Networks and Convolution Neural Networks. The combined model developed receives meta-data, such as the number of words in the review and the number of likes the review receives in addition to the key review words. The two models, the first of which predicts polarity and the second reviews ratings, were tested under several variations of parameters and showed consistency in results. The dataset was collected from Google Maps and focused on two crowded airports in the Arabic Peninsula (Doha and Dubai). They were found to be unbalanced, with positive reviews being more abundant than negative reviews.

Keywords

• Airport service quality
• Passenger review rating
• Deep learning