Understanding Customer's Evaluations Through Mining Airline Reviews

Yakut, İbrahim; Turkoglu, Tugba; Yakut, Fikriye

doi:10.5121/ijdkp.2015.5601

Cited by 12 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To that end, we perform clustering and cluster labeling of the review content in order to identify topics which are discussed in reviews. In contrast to [19], we do not cluster the content with the commonly used k-means approach but rather using Suffix Tree Clustering (STC) [21], an approach that focuses on the problem of cluster labeling. We justify our choice since this clustering technique merges base clusters with high textual overlaps and was shown to outperform group average agglomerative hierarchical clustering, k-means, buckshot, fractionation and single-pass algorithms [21,14].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, this data may be a valuable source for researchers that aim at better understanding the factors that contribute to user satisfaction. One recent work going into that direction is the one described in [19], in which the authors mined review data about airlines' inflight services from the Skytrax portal. By grouping travelers via feature-based and clustering-based modelling, the authors showed that inferences can be captured to explain how travelers evaluate in-flight services.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Lacic¹,

Kowald²,

Lex³

2016

Preprint

View full text Add to dashboard Cite

Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Lacic¹,

Kowald²,

Lex³

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…He applied eight machine learning models for patient complaint classification using the explored rules to achieve significantly higher results as compared with basic unigram features using the same models. Yakut et al [9] explored customer review data for in-flight services of airline companies and draw customer models with respect to such data. He applied two modelling techniques as feature-based modelling and clustering-based modelling.…”

Section: Related Workmentioning

confidence: 99%

Text Mining Techniques for Intelligent Grievances Handling System: WECARE Project Improvements in EgyptAir

Al-Tabbakh¹,

Mohammed²,

El-Zahed³

2019

ijacsa

View full text Add to dashboard Cite

The current work provides quick responding and minimize the required time of processing of the incoming grievances by using automated categorization that analyses the English text contents and predict the category. This work built a model by text mining and NLP processing to extract the useful information from customer grievances data to be used as a guideline to air transport industry. A customer grievances' system in EGYPTAIR called WECARE has had large feeds of data which can be collected in data sets through various channels such as e-mail, website or mobile Apps. Then the incoming data sets are analyzed and assessed by organization's staff then it is assigned to related department through manual classification. Finally, it provides proposed solution for the issue. Thence grievances categorization that handled manually is time consuming process. So, this work decided a model to improve WECARE system in Egypt Airlines. Classification based data mining Techniques are used to identify data into groups of categories across the variable touch points. The system has 166 categories of problems, but for experimental purposes we decided to study six categories only. We have applied four commonly used classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayesian and Decision Tree on our data set to classify the grievances data set then selecting the best of them to be the candidate grievances classifier in enhanced WECARE system. Among four classifiers applied on the dataset, KNN achieved the highest average accuracy (97.5%) with acceptable running time. Also, the work is extended to make hint to the system user, about how to solve this grievance issue based on previous issues saved in Knowledge Base (KB). Several experiments were conducted to test solution hint module by changing similarity score. The benefits of performing a thorough analysis of problems include better understanding of service performance.

show abstract

“…An increase in the number of customers who stay this can happen if customers are satisfied with the service facilities offered by the company. Services that do not meet the quality will be very easily abandon and eventually the customer will switch to another company (Yakut, Turkoglu, and Yakut, 2015;Dhandayudam and Krishnamurthi, 2013;Salajegheh, 2016;Li, Tang, Luo, and Xu, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Where the Rough Set method can classify the data and is quite effective in the search knowledge. The resulting General Rule will depict which of these four dimensions most dominant affects customer satisfaction, so that the company can manage the strategy in creating customer satisfaction in the future (Stefanowski, 1998;Yakut et al, 2015;Jiang, Kwong, Law, and Ip, 2013).…”

Section: Introductionmentioning

confidence: 99%

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Istianingsih¹,

Defit²

2021

IJEBA

View full text Add to dashboard Cite

Purpose: The research aims to determine how the level of customer satisfaction in a small business is a field of flower arrangement services as well as information that can dug from a collection of customer data. Approach/Methodology/Design: The samples used were ten customers who considered loyal. The method used is a Rough set with a satisfaction attribute consisting of confidence, integrity, pride, and desire. Findings: Based on this decision rule, confidence becomes the most dominant attribute. The results showed that the four dimensions of the emotional bond used as an attribute in the Rough Set process to analyze the level of customer satisfaction that strongly affects is the dimension of Confidence, this is evident from the resulting General Rule. Every decision produced always uses the confidence dimension in the comparison input, meaning the level of customer confidence in the company should be a special concern. In the dimensions of the emotional bonds the first dimensions are constructed and fundamental is the confidence dimension, this dimension indicates the level of customer confidence in the company. Confidence dimensions cannot stand it self yet to build long-term relationships with customers without by other dimensions. Practical Implications: The study will contribute positively to give some information of knowledge that contained on customer satisfaction. It shown the alternative for measuring for satisfaction that can be used for a small business and academic. Originality/Value The results generated in this study opened new avenues for customer satisfaction analysis. We must not limit the analysis of customer satisfaction survey data using conventional statistical methods. The Rough theory Set is an innovative tool for finding Knowledge of customer behaviour patterns.

show abstract

Understanding Customer's Evaluations Through Mining Airline Reviews

Cited by 12 publications

References 13 publications

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review

Text Mining Techniques for Intelligent Grievances Handling System: WECARE Project Improvements in EgyptAir

Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction

Contact Info

Product

Resources

About