  The online environment has provided a great opportunity for insurance
policyholders to share their complaints with respect to different services.
These complaints can reveal valuable information for insurance companies who
seek to improve their services; however, analyzing a huge number of online
complaints is a complicated task for human and must involve computational
methods to create an efficient process. This research proposes a computational
approach to characterize the major topics of a large number of online
complaints. Our approach is based on using the topic modeling approach to
disclose the latent semantic of complaints. The proposed approach deployed on
thousands of GEICO negative reviews. Analyzing 1,371 GEICO complaints indicates
that there are 30 major complains in four categories: (1) customer service, (2)
insurance coverage, paperwork, policy, and reports, (3) legal issues, and (4)
costs, estimates, and payments. This research approach can be used in other
applications to explore a large number of reviews.
