Kutuplaşmış Misafir Deneyimleri Karşısında Çevrim İçi İtibar Yönetimi: Lüks Otel Yorumlarından Kanıtlar

Abstract

Bu çalışma; Türkiye'nin dört büyük turizm ve iş şehri olan İstanbul, Ankara, İzmir ve Antalya'daki lüks otellerin çevrimiçi müşteri yorumlarını ve otel yönetimlerinin yanıtlarını incelemektedir. Aynı otel için çok olumlu ve son derece olumsuz yorumların bir arada bulunması, kritik bir sorunu vurgulamaktadır: misafir beklentileri ile hizmet sunumu arasındaki uyumsuzluk. Geleneksel anketlerde genellikle gözden kaçan bu kutuplaşmış deneyimleri daha iyi anlamak için çalışma, NLP (Doğal Dil İşleme) kullanarak içerik analizi ve duygu analizini birleştiren karma yöntemli bir yaklaşım benimsemiştir. TripAdvisor'dan toplam 200 müşteri yorumu ve bunlara karşılık gelen yönetim yanıtları analiz edilmiştir. Tüm yorumlar Ocak ile Haziran 2025 tarihleri arasında toplanmış olup, bunların 177’si yönetici yanıtı içerirken 23’ü yanıtsız kalmıştır. Sonuçlar; hizmet kalitesindeki tutarsızlıkların, personel etkileşimindeki farklılıkların, altyapı sorunlarının ve çeşitli yanıt stratejilerinin kutuplaşmaya katkıda bulunduğunu göstermektedir. Yönetim yanıtları ayrıca otellerin dijital şikâyetleri nasıl ele aldığını değerlendirmek için içerik ve üslup açısından kategorilere ayrılmıştır. Çalışma "kutuplaşmış deneyimler" kavramını tanıtarak ve çevrimiçi yorum analizinin konaklama araştırmalarında geleneksel yöntemlere göre daha derin bilgiler sağlayabileceğini göstererek literatüre katkıda bulunmaktadır.

Keywords

• Çevrimiçi Yorumlar
• Dijital Şikayetler
• Doğal Dil İşleme
• Duygu Analizi
• kutuplaşmış deneyimler

Managing Online Reputation amid Polarized Guest Experiences: Evidence from Luxury Hotel Reviews in Turkiye

Abstract

This study examines online customer reviews and hotel management responses from luxury hotels in Turkey’s four major tourism and business cities: Istanbul, Ankara, Izmir, and Antalya. The co-occurrence of very positive and highly negative reviews for the same hotel highlights a critical issue: the mismatch between guest expectations and service delivery. To better understand these polarized experiences—often overlooked by traditional surveys—the study adopts a mixed-methods approach combining content analysis and sentiment analysis using NLP (Natural Language Processing). A total of 200 customer reviews and their corresponding managerial responses from TripAdvisor were analyzed. Results show that inconsistencies in service quality, differences in staff interaction, infrastructure problems, and varied response strategies contribute to polarization. Management responses were also categorized by content and tone to evaluate how hotels handle digital complaints. The study contributes to the literature by introducing the concept of “polarized experiences” and showing that online review analysis can provide deeper insights than conventional methods in hospitality research.

Keywords

• Natural Language Processing
• Polarized experiences
• Digital Complaints
• Sentiment Analysis
• Online Review

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