论文标题
从在线客运评论中发现特定于航空公司的商业智能：一种无监督的文本分析方法
Discovering Airline-Specific Business Intelligence from Online Passenger Reviews: An Unsupervised Text Analytics Approach
论文作者
论文摘要
为了从乘客的角度来了解服务质量的重要方面，以及量身定制的服务产品以获得竞争优势，航空公司可以利用丰富的在线客户评论（OCR）。本文的目的是使用无监督的文本分析方法从OCR发现公司和竞争对手的智能。首先，使用三种主题模型（概率潜在的语义分析（PLSA））和两个潜在的dirichlet分配（LDA-VI和LDA-GS）提取了OCR中讨论的关键方面（或主题）。随后，我们提出了一个集成的主题模型（EA-TM），该模型集成了各个主题模型，以将每个审查句子分类为最具代表性的方面。同样，为了确定与审查句子相对应的情绪，开发了三种意见挖掘方法（Afinn，Sentistrength和vader）的预测合奏情感分析仪（E-SA）。基于方面的意见摘要（AOS）是通过巩固与各个方面相关的情感来建立的，它提供了乘客感知的优势和劣势的快照。此外，对标记的OCR进行了BI-GRAM分析，以在每个确定的方面进行根本原因分析。一项涉及99,147个对美国目标载体及其四个竞争对手的航空公司审查的案例研究用于验证拟议的方法。结果表明，可以从OCR获得一家航空公司及其竞争对手的成本和时间效率的绩效摘要。最后，除了根据我们的结果提供理论和管理意义外，考虑到冠状病毒疾病2019（COVID-19）的前所未有的影响以及对未来类似大流行病的预测，我们还为航空行业的流行后准备性提供了影响。
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.
