A Proposed Service Quality Measurement Model using Sentiment Analysis and Text Mining: The Case of Water and Sewerage Services

Erhan Sur; Hüseyin Çakır

Makale detayları

A Proposed Service Quality Measurement Model using Sentiment Analysis and Text Mining: The Case of Water and Sewerage Services

Yazarlar:

Erhan Sur | Hüseyin Çakır

Yayıncı:

İdeal Kent Yayınları

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

OPUS Toplum Araştırmaları Dergisi Vol.20 Issue 54, pp.473-487

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293430

pISSN:

2791-9862

Anahtar kelimeler:

Özet:

 This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the models purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords flood, meter, rain, irrigation, infrastructure, sewerage, sewage, maintenance hole , aski, waterless, water were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

 This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the models purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords flood, meter, rain, irrigation, infrastructure, sewerage, sewage, maintenance hole , aski, waterless, water were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

 This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the models purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords flood, meter, rain, irrigation, infrastructure, sewerage, sewage, maintenance hole , aski, waterless, water were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

 Bu çalışmada, duygu analizi ve metin madenciliği teknikleri kullanılarak hizmet kalitesi ölçümü için yeni bir model önerilmiştir. Bu model, geleneksel yöntemlerin karşılaştığı zaman, maliyet ve uygulama güçlüğünün üstesinden gelmeyi ve hizmet kalitesi ölçümüne daha dinamik ve verimli bir yaklaşım sağlamayı amaçlamıştır. Ayrıca bu modelde SERVQUAL veya SERVPERF gibi hizmet kalitesi ölçümünde kullanılan boyutların yerine metin madenciliği ile modelin kullanıldığı hizmet sektörüne özel yeni kategoriler ve anahtar kelimelerin nasıl belirleneceği gösterilmiştir. Böylelikle hizmet kalitesi ölçümünde daha doğru sonuçlara ulaşılması hedeflenmemiştir. Çalışmada önerilen modelin amacına ulaşabilmesi için metin madenciliği ve duygu analiziyle işlenen sosyal medya verilerinden hizmet kalitesi ölçüm modelinin nasıl geliştirileceği sorusuna yanıt aranmıştır. Bu soruya yanıt bulabilmek için bir belediyenin Twitter hesabına 2016-2022 yılları arasında gönderilen 109.844 tweetden metin madenciliği yöntemi ile belediyenin vermiş olduğu su ve kanalizasyon hizmetleriyle ilgili olarak sel, sayaç, yağmur, sulama, altyapı, kanalizasyon, lağım, rögar, aski, susuz, suya anahtar kelimeleri çıkartılmıştır. Çıkartılan anahtar kelimelerin geçtiği 5766 tweet duygu analizine tabi tutularak hizmet kalitesi ölçümü gerçekleştirilmiştir. Yapılan hizmet kalitesi ölçüm neticesinde 1922 olumsuz, 973 olumlu ve 2871 nötr tweet tespit edilmiştir. Ortalama olumsuz puan 0,51, ortalama olumlu puan 0,11 ve ortalama nötr puan 0,38 olarak hesaplanmıştır.

Makale detayları

A Proposed Service Quality Measurement Model using Sentiment Analysis and Text Mining: The Case of Water and Sewerage Services

Yazarlar:

Erhan Sur | Hüseyin Çakır

Yayıncı:

İdeal Kent Yayınları

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

OPUS Toplum Araştırmaları Dergisi Vol.20 Issue 54, pp.473-487

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293430

pISSN:

2791-9862

sentiment analysis

text mining

service quality

twitter data analysisduygu analizi

metin madenciliği

hizmet kalitesi

twitter veri analizi

Sayı

Makale detayları

Yazarlar: Erhan Sur | Hüseyin Çakır

Yayıncı: İdeal Kent Yayınları

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: OPUS Toplum Araştırmaları Dergisi Vol.20 Issue 54, pp.473-487

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001293430

pISSN: 2791-9862

Sayı

Yazarlar:

Erhan Sur | Hüseyin Çakır

Yayıncı:

İdeal Kent Yayınları

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

OPUS Toplum Araştırmaları Dergisi Vol.20 Issue 54, pp.473-487

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001293430

pISSN:

2791-9862