Evaluating airline passengers’ satisfaction during the COVID-19 pandemic: a text mining approach

Riantama, Dalianus; Tsai, An-Lun; Lin, Shan-Fong; Lin, Horn-Tzer

doi:10.21428/7a45813f.d5497ab0

Cited by 2 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(FN) false negatives: the prediction is no, but passengers are satisfied. Accuracy was determined by calculating true positives and true negatives divided by the total values of the confusion matrix [36,37]. This value was calculated using Eq.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers' expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers' pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deeplearning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

show abstract

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Riantama et al [36] stated that satisfying customers' needs is crucial for achieving loyalty. They evaluated the factors of service quality and customer satisfaction in the airline industry.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Kim et al, 2020). Fierce competition in the airline industry demands efficient customer relations management either online or offline to bring future income (Riantama et al, 2021). Online customer reviews can be a solution to manage customer relations to know what customers think of the airline.…”

Section: Introductionmentioning

confidence: 99%

What are the Crucial Factors in Gaining Customer Recommendations in the Airline Industry?

Riantama

2023

View full text Add to dashboard Cite

The airline business is continually growing. This study tries to find out important factors for customers to give their recommendations for an airline. Text mining approach and feature selection methods are applied to clean the data set and to select the most crucial factors of customer recommendations. This study collected 846 online customer reviews for full-service airlines and 179 low-cost carrier airlines. Cleanliness and crew care are the most critical factors for full-service airline customers. Low-cost carrier airline customers were more concerned about the value of money and flight information. The results provide airlines’ managers with new insights to make their customers willing to give recommendations and gain a positive brand image. Keywords: Online customers review, text mining, customer recommendations, feature selection, airline

show abstract

Evaluating airline passengers’ satisfaction during the COVID-19 pandemic: a text mining approach

Cited by 2 publications

References 32 publications

An Optimized Deep Learning Approach for Improving Airline Services

An Optimized Deep Learning Approach for Improving Airline Services

What are the Crucial Factors in Gaining Customer Recommendations in the Airline Industry?

Contact Info

Product

Resources

About