Based on 42,063 airport reviews collected from Google Maps, we conducted a sentiment analysis and a topic modeling. We showed that the sentiment scores computed from textual reviews are good estimates of their paired star-ratings (r=0.63, p<0.01). Next, using the LDA (Latent Dirichlet Allocation), we extracted latent topics from the textual reviews and compared them with the standard categories utilized in the Airport Service Quality survey (ASQ). The topics extracted from reviews correspond well with the categories used in ASQ. We, in turn, compared the online ratings with the ratings annually updated by ASQ. While online reviews discuss almost identical topics with those of ASQ, the correlation between the ratings from two was weak (r=0.2). We suggest that the text mining approach using online reviews not only provides an inexpensive, dynamic, and locally customizable means of monitoring airport quality but also complements the standard survey by offering an alternative metric. ><0.01). Next, using the LDA (Latent Dirichlet Allocation), we extracted latent topics from the textual reviews and compared them with the standard categories utilized in the Airport Service Quality survey (ASQ). The topics extracted from reviews correspond well with the categories used in ASQ. We, in turn, compared the online ratings with the ratings annually updated by ASQ. While online reviews discuss almost identical topics with those of ASQ, the correlation between the ratings from two was weak (r=0.2). We suggest that the text mining approach using online reviews not only provides an inexpensive, dynamic, and locally customizable means of monitoring airport quality but also complements the standard survey by offering an alternative metric.