Online Otel Yorumlarının Metin Madenciliği Teknikleri ile İncelenmesi: Bakü Otelleri Örneği

Year 2022, Volume: 1 Issue: 1, 1 - 8, 02.04.2022

Erkan Akgöz Engin Tengilimoğlu

Abstract

İletişim teknolojilerinde yaşanan gelişmelerin tüketicilere satın alma ve satın aldıkları ürünler hakkında deneyimlerini paylaşma imkanı sunması sonucunda online platformlar turizm sektörünü derinden etkileyen unsurlara dönüşmüştür. Tüketicilerin online ortamlarda giderek daha fazla yorum paylaşması sonucunda he tüketiciler hem de tedarikçiler için değerli bilgiler içeren büyük veri yığınları oluşmuştur. Bu büyük verinin analiz edilme ihtiyacı ise büyük veri analiz yöntemlerinin online üzerinde kullanılmaya başlamasına neden olmuştur. Bu çalışmada ise booking.com üzerinde yer alan 5 yıldızlı Bakü otellerine ilişkin 3.275 adet yorum büyük veri analiz yöntemlerinden olan metin madenciliği teknikleri kullanılarak incelenmiştir. Yorumlar veri ön işleme sürecine tabi tutulmuş ve sonrasında kelime sıklık ve ağırlıkları hesaplanmıştır. Daha sonra birliktelik analizleri kullanılarak hangi terimlerin birlikte kullanıldığı tespit edilmiştir. Sonuç olarak yorumlarda personel, oda ve otel terimlerinin en sık kullanılan terimler olduğu saptanmıştır.

Keywords

Online Yorumlar, Metin Madenciliği, Kelime Sıklıkları, Metin Analizi

Investigation of Online Hotel Reviews with Text Mining Techniques: The Case of Baku Hotels

Abstract

Online environments turn into platforms that influence the tourism sector deeply as a result of advancements in communication technologies to give customer a chance to buy tourism products online and write reviews about their experience. As more and more travelers contribute their travel experience on travel websites, a huge amount of hotel reviews is generated daily. The reviews shared daily on these platforms constitute big data that contain useful information for both customers and suppliers. The necessity of analyzing these big data causes to use of data mining techniques in tourism research. In this research text mining techniques which is a natural extension of data mining used to examine 3.275 Baku hotels’ reviews from booking.com. First reviews are prepared for analyzing with using the preprocessing technique. Then term frequency was constituted by using the TF-IDF technique. Consequently, it is found that staff, room, and hotel are the most spoken topics on hotel reviews.

Keywords

Online Reviews, Text Mining, Term Frequency

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