Implicit Aspect-Based Opinion Mining and Analysis of Airline Industry Based on User-Generated Reviews

Abstract

Mining opinions from reviews has been a field of ever-growing research. These include mining opinions on document level, sentence level and even aspect level. While explicitly mentioned aspects from user-generated texts have been widely researched, very little work has been done in gathering opinions on aspects that are implied and not explicitly mentioned. Previous work to identify implicit aspects and opinion was limited to syntactic-based classifiers or other machine learning methods trained on restaurant dataset. In this paper, the present is a novel study for extracting and analysing implicit aspects and opinions from airline reviews in English. Through this study, an airline domain-specific aspect-based annotated corpus, and a novel two-way technique that first augments pre-trained word embeddings for sequential with stochastic gradient descent optimized conditional random fields (CRF) and second using machine and ensemble learning algorithms to classify the implied aspects is devised and developed. This two-way technique resolves double-implicit problem, most encountered by previous work in implicit aspect and opinion text mining. Experiments with a hold-out test set on the first level i.e., entity extraction by optimized CRF yield a result of ROC-AUC score of 96% and F1 score of 94% outperforming few baseline systems. Further experiments with a range of machine and ensemble learning classifier algorithms to classify implied aspects and opinions for each entity yields a result of ROC-AUC score ranging from 71 to 94.8% for all implied entities. This two-level technique for implicit aspect extraction and classification outperforms many baseline systems in this domain.

A Systematic Review on Airlines Industries based on Sentiment Analysis and Topic Modeling

“…Firstly, methods that lack training data are mainly due to language barriers and airline dependency [ [39], [48], [86], [74]]. Travelers often feel more compelled to put negative reviews, resulting in small, imbalanced [ [26], [28], [50], [14]] training data. Secondly, lack of preprocessing [54], not conducting training with shortlisted feature sets [ [12], [58]] and utilization of entire feature space [ [27], [51]] while creating split trees epitomize limitations in training.…”

Ensemble BiLSTM: A Novel Approach for Aspect Extraction From Online Text

“…It can be achieved by using different architectures or models with distinct characteristics or by creating variations of the same model through different training strategies, subsets of the training data, or randomisation techniques. Verma & Davis (2021) [26] utilised ensemble learning with different models and techniques to capture diverse aspect features and improve extraction accuracy. Ensemble methods are suitable for text data, which lacks a clear hierarchical structure.…”

Evaluation of passengers' expectations and satisfaction in the airline industry: an empirical performance analysis of online reviews

“…Previous UGC research has covered a wide range of topics, such as: examining the relationship between online reviews and improved firm performance (Camis on and Villar-L opez, 2014), identifying product defects (Abrahams et al, 2015), examining hotel reviews and responses (Chang et al, 2020;Barreda and Bilgihan, 2013), studying factors impacting intention to use travel and food delivery services , product development (Ho-Dac, 2020), examining fashion consumer online experience (Vazquez et al, 2021) and analysis of healthcare marketing (Cuomo et al, 2020). A stream of research has also focused on the UGC assessment of the airline industry, such as: Loo (2020) which examined the airline companies' engagement with their passengers, Verma and Davis (2021) which extracted and investigated implicit aspects and opinions from airline reviews, and Rasool and Pathania (2021) which used sentiment analysis to investigate low-cost commercial airline service quality. Examining consumers' expectations and satisfaction with airline services is a growing and still underexplored research topic (Han et al, 2020;Halpern and Mwesiumo, 2021;Sudhakar and Gunasekar, 2020).…”

“…, 2020). A stream of research has also focused on the UGC assessment of the airline industry, such as: Loo (2020) which examined the airline companies' engagement with their passengers, Verma and Davis (2021) which extracted and investigated implicit aspects and opinions from airline reviews, and Rasool and Pathania (2021) which used sentiment analysis to investigate low-cost commercial airline service quality. Examining consumers' expectations and satisfaction with airline services is a growing and still underexplored research topic (Han et al.…”