DUYGU ANALİZİ İLE OTEL MÜŞTERİLERİNİN YILDIZ DEĞERLENDİRMELERİNİN TAHMİNİ

Year 2020, Volume: 6 Issue: 1, 86 - 95, 15.06.2020

Selçuk Kıran Meltem Özturan

Abstract

Bu çalışmanın üç farklı amacı vardır; oteller ve derecelendirmeleri hakkında bazı bilgileri keşfetmek, müşterilerin eWOM yorumları için duygu analizi ile yıldız derecelendirmelerini tahmin etmek ve bu eWOM yıldız derecelendirmelerini istatistiksel analizlerle söz konusu yorumlara karşılık gelen çevrimiçi yıldız derecelendirmeleriyle karşılaştırmaktır. Farklı gelir seviyesine sahip dört ildeki otel verileri PHP ile yazılmış bir program kullanılarak toplanmıştır. Veriler çeşitli kurallar göz önünde bulundurularak temizlenip, analizlere hazır hale getirilmiştir. Lexalytics aracının akademik demo sürümü, duyarlılık analizi, RStudio ise istatistik analizler için kullanılmıştır. Sonuçlar, otel başına ortalama oda sayısının ve günlük ortalama otel ücretlerinin illerin gelir düzeyi arttıkça arttığını, otellerin yıldız puanlarının ise illerin gelir düzeyi azaldıkça arttığını göstermektedir. Analizler tüm iller için eWOM ve çevrimiçi yıldız derecelendirmeleri arasında anlamlı bir fark olduğunu ve ayrıca tüm iller için bu iki yıldız derecelendirme arasında anlamlı ve orta düzeyde bir pozitif ilişki olduğunu göstermektedir.

Keywords

duygu analizi, eWOM, yıldız derecelendirmesi, otel, gelir düzeyi

PREDICTION OF STAR RATINGS OF HOTEL CUSTOMERS USING SENTIMENT ANALYSIS

Abstract

The aim of this study is three-fold; access some exploratory findings about hotels and their ratings, predict star ratings of customers by sentiment analysis using their eWOM comments, and compare predicted eWOM star ratings of customers with the corresponding online star ratings by statistical analyses. Data of hotels from four cities with different income levels are retrieved using a script written with PHP. The data are cleaned considering various rules to be ready for analyses. Academic demo version of Lexalytics tool is used for sentiment analysis and RStudio for statistical analyses. Results show that, average number of rooms per hotel and their daily average rates increase as the income level of cities increase whereas star ratings of hotels increase as the income level of cities decrease. Analyses show that that for all cities, there is a significant difference between eWOM and online star ratings and also a significant moderate positive relationship between these two star ratings for all cities.

Keywords

sentiment analysis, eWOM, star rating, hotel, income level

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