Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

Year 2023, , 473 - 487, 31.07.2023

Erhan Sur , Hüseyin Çakır

https://doi.org/10.26466/opusjsr.1272020

Abstract

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 tweet’den 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.

Keywords

duygu analizi, metin madenciliği, hizmet kalitesi, twitter veri analizi

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

Abstract

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 model’s 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.

Keywords

sentiment analysis, text mining, service quality, twitter data analysis

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