  To understand the important dimensions of service quality from the
passenger's perspective and tailor service offerings for competitive advantage,
airlines can capitalize on the abundantly available online customer reviews
(OCR). The objective of this paper is to discover company- and
competitor-specific intelligence from OCR using an unsupervised text analytics
approach. First, the key aspects (or topics) discussed in the OCR are extracted
using three topic models - probabilistic latent semantic analysis (pLSA) and
two variants of Latent Dirichlet allocation (LDA-VI and LDA-GS). Subsequently,
we propose an ensemble-assisted topic model (EA-TM), which integrates the
individual topic models, to classify each review sentence to the most
representative aspect. Likewise, to determine the sentiment corresponding to a
review sentence, an ensemble sentiment analyzer (E-SA), which combines the
predictions of three opinion mining methods (AFINN, SentiStrength, and VADER),
is developed. An aspect-based opinion summary (AOS), which provides a snapshot
of passenger-perceived strengths and weaknesses of an airline, is established
by consolidating the sentiments associated with each aspect. Furthermore, a
bi-gram analysis of the labeled OCR is employed to perform root cause analysis
within each identified aspect. A case study involving 99,147 airline reviews of
a US-based target carrier and four of its competitors is used to validate the
proposed approach. The results indicate that a cost- and time-effective
performance summary of an airline and its competitors can be obtained from OCR.
Finally, besides providing theoretical and managerial implications based on our
results, we also provide implications for post-pandemic preparedness in the
airline industry considering the unprecedented impact of coronavirus disease
2019 (COVID-19) and predictions on similar pandemics in the future.
