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.
翻译:为了从乘客的角度理解服务质量的重要层面,并为了竞争优势而定制服务,航空公司可以利用现有的大量在线客户审查(OCR),本文的目的是利用不受监督的文本分析方法,从OCR发现公司和竞争者特有的情报。首先,OCR讨论的关键方面(或议题)采用三个主题模型——概率潜在暗语分析(PLSA)和“利登迪里特147”分配的两个变式(LDA-VI和LDA-GS)。随后,我们提议了一个联合辅助主题模型(EA-TM),该模型将单个主题模型整合起来,将每项审查句子分为最有代表性的方面。同样,确定与审评判决对应的情绪(E-SA),它综合了三种意见采矿方法的预测(AFINN、SentiStrength和VADER)。基于面的观点摘要(AOS),它提供了客机-线性辅助主题主题主题模型模型(EA-TM),该模型提供了客机机精度分析的准确性影响和机能分析的每一项分析。