保险投诉的计算分析：GEICO案例研究
Computational Analysis of Insurance Complaints: GEICO Case Study
摘要：
在线环境为保险投保人提供了一个很好的机会，分享他们对不同服务的投诉。这些投诉可以为寻求改善服务的保险公司提供有价值的信息;然而，分析大量在线投诉对人类来说是一项复杂的任务，必须涉及计算方法才能创建一个有效的过程。本研究提出了一种计算方法来描述大量在线投诉的主要主题。我们的方法是基于使用主题建模的方法来揭示潜在的语义投诉。所提出的方法部署在数以千计的GEICO负面评论。对1,371起GEICO投诉的分析表明，有30起主要投诉分为四类：（1）客户服务;（2）保险范围、文书工作、政策和报告;（3）法律问题;（4）成本、估计和付款。这种研究方法可以用于其他应用程序，以探索大量的评论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
