Atıf İçin Kopyala
Yiğit Açıkgöz F., Kayakuş M., Moiceanu G., Sönmez N.
SUSTAINABILITY, cilt.16, sa.22, ss.1-19, 2024 (SCI-Expanded)
-
Yayın Türü:
Makale / Tam Makale
-
Cilt numarası:
16
Sayı:
22
-
Basım Tarihi:
2024
-
Doi Numarası:
10.3390/su16229610
-
Dergi Adı:
SUSTAINABILITY
-
Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, Agricultural & Environmental Science Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
-
Sayfa Sayıları:
ss.1-19
-
Akdeniz Üniversitesi Adresli:
Evet
Özet
This study investigates the assessment of sustainable corporate reputation through citizen comments and how it can be measured by sentiment analysis methods based on machine learning and text mining. The research analyses citizen feedback on municipalities in the field of public services and examines their impact on the social reputation of the services provided by municipalities. Support vector machines, one of the machine learning methods, was used for sentiment analysis. In the study, Google Maps comments of the citizens receiving services from the municipality were used. The results of the sentiment analysis reveal that sustainable corporate reputation is directly related to citizen satisfaction and feedback. In this context, municipalities should continuously receive feedback and make strategic improvements based on citizens’ comments to ensure sustainable service quality. Municipalities are especially appreciated by citizens for their fast, effective, and high-quality services. However, some negative comments focus on issues such as the slowness of services, cleaning problems, and staff attitudes, indicating that certain improvements are needed. This feedback emphasises the need for continuous improvement in service quality.
This study investigates the assessment of sustainable corporate reputation through citizen comments and how it can be measured by sentiment analysis methods based on machine learning and text mining. The research analyses citizen feedback on municipalities in the field of public services and examines their impact on the social reputation of the services provided by municipalities. Support vector machines, one of the machine learning methods, was used for sentiment analysis. In the study, Google Maps comments of the citizens receiving services from the municipality were used. The results of the sentiment analysis reveal that sustainable corporate reputation is directly related to citizen satisfaction and feedback. In this context, municipalities should continuously receive feedback and make strategic improvements based on citizens’ comments to ensure sustainable service quality. Municipalities are especially appreciated by citizens for their fast, effective, and high-quality services. However, some negative comments focus on issues such as the slowness of services, cleaning problems, and staff attitudes, indicating that certain improvements are needed. This feedback emphasises the need for continuous improvement in service quality.