Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints
Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reve...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/5/325 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126946846900224 |
|---|---|
| author | Huali Cai Tao Dong Pengpeng Zhou Duo Li Hongtao Li |
| author_facet | Huali Cai Tao Dong Pengpeng Zhou Duo Li Hongtao Li |
| author_sort | Huali Cai |
| collection | DOAJ |
| description | Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. |
| format | Article |
| id | doaj-art-dbeb2c3d26e14529aa5a5978b49aeef8 |
| institution | OA Journals |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-dbeb2c3d26e14529aa5a5978b49aeef82025-08-20T02:33:48ZengMDPI AGSystems2079-89542025-04-0113532510.3390/systems13050325Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger ComplaintsHuali Cai0Tao Dong1Pengpeng Zhou2Duo Li3Hongtao Li4China Academy of Civil Aviation Science and Technology, Chaoyang District, Beijing 100028, ChinaSchool of Economics and Management, China University of Petroleum, Beijing 102249, ChinaChina Academy of Civil Aviation Science and Technology, Chaoyang District, Beijing 100028, ChinaSchool of Economics and Management, China University of Petroleum, Beijing 102249, ChinaChina Academy of Civil Aviation Science and Technology, Chaoyang District, Beijing 100028, ChinaAirline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports.https://www.mdpi.com/2079-8954/13/5/325airline managementcivil aviationcomplaint analysistext miningsentiment analysistopic modeling |
| spellingShingle | Huali Cai Tao Dong Pengpeng Zhou Duo Li Hongtao Li Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints Systems airline management civil aviation complaint analysis text mining sentiment analysis topic modeling |
| title | Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints |
| title_full | Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints |
| title_fullStr | Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints |
| title_full_unstemmed | Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints |
| title_short | Leveraging Text Mining Techniques for Civil Aviation Service Improvement: Research on Key Topics and Association Rules of Passenger Complaints |
| title_sort | leveraging text mining techniques for civil aviation service improvement research on key topics and association rules of passenger complaints |
| topic | airline management civil aviation complaint analysis text mining sentiment analysis topic modeling |
| url | https://www.mdpi.com/2079-8954/13/5/325 |
| work_keys_str_mv | AT hualicai leveragingtextminingtechniquesforcivilaviationserviceimprovementresearchonkeytopicsandassociationrulesofpassengercomplaints AT taodong leveragingtextminingtechniquesforcivilaviationserviceimprovementresearchonkeytopicsandassociationrulesofpassengercomplaints AT pengpengzhou leveragingtextminingtechniquesforcivilaviationserviceimprovementresearchonkeytopicsandassociationrulesofpassengercomplaints AT duoli leveragingtextminingtechniquesforcivilaviationserviceimprovementresearchonkeytopicsandassociationrulesofpassengercomplaints AT hongtaoli leveragingtextminingtechniquesforcivilaviationserviceimprovementresearchonkeytopicsandassociationrulesofpassengercomplaints |