Applying deep learning models to twitter data to detect airport service quality

Barakat, Huda Mohammed; Yeniterzi, Reyyan; Martín-Domingo, Luis

doi:10.1016/j.jairtraman.2020.102003

Cited by 35 publications

(39 citation statements)

References 21 publications

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“…However, secondary data is an alternative data source that has recently been used in ASQ assessment [15,34]. These data, derived from passengers sharing their experiences regarding airport services across various platforms, are less susceptible to the social desirability effect and have broad representativeness [28,35]. Methodologically speaking, it is clear that ASQ research employs a variety of advanced techniques, including CA, QFD, DLM and OLR.…”

Section: Literature Review and Hypotheses Formulation 21 Airport Serv...mentioning

confidence: 99%

“…Online reviews reflecting customers' experiences provide information about products, services and brands and allow customers to voice their experiences [37]. Customers share their service experiences on public online platforms such as Google and Twitter, as well as on travel-related platforms such as TripAdvisor and Skytrax [28]. Via these platforms, it is always possible to access online reviews that are independent of time and place and have a high spread rate [38].…”

Section: Online Passenger Reviewsmentioning

confidence: 99%

“…Online reviews have been used as secondary data in many areas of marketing applications, ranging from pricing [41] to brand management [42]. With the widespread use of Web 2.0 in recent years, online customer reviews have been suggested as an alternative method for assessing ASQ, as passengers express their opinions more frequently about service providers (i.e., airports) [28,40]. As a result, large volumes of online reviews amplify customers' voices, allowing for more effective ASQ strategies to be monitored and developed [39].…”

Section: Online Passenger Reviewsmentioning

confidence: 99%

See 2 more Smart Citations

How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

et al. 2022

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Delivering high-quality service to passengers can be critical for an airport’s survival, competitiveness, profitability, and long-term growth in a highly competitive environment. The present study aims to examine the relationship between airport service attributes and passenger satisfaction. To this end, we conducted multi-method research consisting of symmetric (multiple regression analysis—MRA) and asymmetric (necessary condition analysis—NCA) approaches. The research data consists of 1463 valid online reviews (n = 1463) of the top 50 busiest airports in Europe retrieved from Skytrax. The MRA was employed to examine the net effect of the eight airport service attributes on passenger satisfaction, while the NCA was used to explore the necessary conditions and level of necessity to achieve passenger satisfaction. Using MRA, the findings reveal that airport staff is the most influential predictor of passenger satisfaction, whereas airport shopping and airport Wi-Fi connectivity do not have a significant effect on passenger satisfaction. Moreover, the NCA results found that six of the eight conditions are necessary to achieve passenger satisfaction at the airport. To complement and comprehend the findings, this study also sheds light on the antecedents underlying airport passenger satisfaction in the post-COVID-19 era using NCA.

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Section: Literature Review and Hypotheses Formulation 21 Airport Serv...mentioning

confidence: 99%

Section: Online Passenger Reviewsmentioning

confidence: 99%

Section: Online Passenger Reviewsmentioning

confidence: 99%

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How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

et al. 2022

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“…Existing service quality literatures using UGC typically employ a sample of UGC to extract features or measures that allow for detecting, describing or predicting meaningful patterns. [25], [26], and [27] utilized tweets to identify polarity directions based on classified service attributes. [28], [29], and [23] collected Skytrax reviews to seek for relationships between service aspects performance and customer satisfaction.…”

Section: Background and Related Workmentioning

confidence: 99%

Use of Electronic Word of Mouth as Quality Metrics: A Comparison of Airline Reviews on Twitter and Skytrax

Lü¹,

Mitra²,

Wang³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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User-generated content (UGC) at online platforms serves as a critical data source in the service industry as it can be accessed in real-time and reflect customers' changing focus on service aspects. Drawing upon the importance-performance analysis framework, we propose a methodology to derive service quality metrics by utilizing the heterogeneous sources of UGC with customized text mining techniques and examining the effectiveness of these quality metrics. UGC data related to major U.S. airlines were collected from non-social media (Skytrax) and social media platforms (Twitter) from 2014 to June 2019. The results suggest that the topic distributions and the UGC-derived weighted service quality (WSQ, which represents the weighted sentiment based on service aspects) significantly vary between the non-social media and social media platforms. In addition, the WSQ scores derived from two platforms are significant indicators of the objective service quality measurement (i.e., airline quality rating) with stronger predictive power from the social media derived WSQ score.

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“…AI and DL can streamline and automate customer services, analytics, machinery maintenance, and many other internal procedures and operations in the aviation industry. The most recent and relevant research contributions related to the use of DL techniques in the aviation industry field can be found in [127][128][129][130][131][132][133][134][135]. A comparison of prominent studies is presented in Table 7.…”

Section: Aviation Industrymentioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

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The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

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Applying deep learning models to twitter data to detect airport service quality

Cited by 35 publications

References 21 publications

How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

How to Achieve Passenger Satisfaction in the Airport? Findings from Regression Analysis and Necessary Condition Analysis Approaches through Online Airport Reviews

Use of Electronic Word of Mouth as Quality Metrics: A Comparison of Airline Reviews on Twitter and Skytrax

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

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