Araştırma Makalesi
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Tiny House Otellere Yönelik Müşteri Yorumlarının Yapay Zekâ ile Kurumsal İtibar Analizi

Yıl 2025, Cilt: 2 Sayı: 4, 19 - 34, 30.12.2025

Öz

Bu çalışma, Türkiye’de faaliyet gösteren altı farklı Tiny House otelin kurumsal itibarını çevrimiçi müşteri yorumları üzerinden analiz etmektedir. Veriler OtelPuan platformundan elde edilmiş, otel isimleri gizlenerek TH1–TH6 şeklinde kodlanmıştır. Araştırmada TextBlob kütüphanesi kullanılarak duygu analizi yapılmış ve yorumlar olumlu, olumsuz ve nötr kategorilere ayrılmıştır. Bulgular, tüm otellerde olumlu yorumların baskın olduğunu, olumsuz yorumların ise sınırlı kaldığını göstermektedir. Özellikle TH3 oteli %93 oranında olumlu duygu ile öne çıkmaktadır. Kelime sıklığı ve kelime bulutu analizleri, olumlu yorumlarda temizlik, personel ilgisi, kahvaltı ve fiyat-performans unsurlarının ön planda olduğunu, olumsuz yorumlarda ise konum, oda düzeni ve teknik eksikliklerin öne çıktığını ortaya koymuştur. Nötr yorumlar ise daha çok betimleyici ve açıklayıcı niteliktedir. Sonuç olarak Tiny House otellerinin dijital ortamda güçlü bir kurumsal itibara sahip olduğu görülmektedir.

Etik Beyan

Mevcut çalışma için mevzuat gereği etik izni alınmaya ihtiyaç yoktur.

Destekleyen Kurum

TÜBİTAK

Proje Numarası

1919B012415353

Teşekkür

Bu çalışma TÜBİTAK Bilim İnsanı Destekleme Daire Başkanlığı (BİDEB) tarafından 2209-A Üniversite Öğrencileri Araştırma Projeleri Destek Programı kapsamında desteklenmiştir (Proje Numarası: 1919B012415353).

Kaynakça

  • Akyol, D. P. (2022). Küçük ev (tiny house) olgusunun alternatif turizme yönelik geçici konaklama mekânı olarak potansiyellerinin değerlendirilmesi: Antalya/İbradı örneği (Yüksek lisans tezi). Bursa Uludağ Üniversitesi.
  • Alotaibi, E. (2020). Application of machine learning in the hotel industry: A critical review. Journal of Association of Arab Universities for Tourism and Hospitality, 18(3), 78–96.
  • Ashqar, R. I., & Ramos, C. M. (2023, Şubat). Machine-learning holistic review in tourism and hospitality. In The International Conference on Global Economic Revolutions (ss. 78–84). Springer Nature Switzerland.
  • Bashiri, H., & Naderi, H. (2024). Comprehensive review and comparative analysis of transformer models in sentiment analysis. Knowledge and Information Systems, 66(12), 7305–7361.
  • Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: Using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386–403.
  • Büyükeke, A. (2025). Dijital platformlarda çevrimiçi itibar yönetimi: Belek ve Kaş bölgelerindeki otellerin müşteri ilişkileri yönetimi stratejilerinin analizi. Sosyal Bilimler Araştırmaları Dergisi, 20(1), 114–143.
  • Chun, R. (2005). Corporate reputation: Meaning and measurement. International Journal of Management Reviews, 7(2), 91–109.
  • Çaylak, P. Ç., Kayakuş, M., Eksili, N., Yiğit Açikgöz, F., Coşkun, A. E., Ichimov, M. A. M., & Moiceanu, G. (2024). Analysing online reviews consumers’ experiences of mobile travel applications with sentiment analysis and topic modelling: The example of Booking and Expedia. Applied Sciences, 14(24), 11800.
  • Erdoğan, D., Kayakuş, M., Çelik Çaylak, P., Ekşili, N., Moiceanu, G., Kabas, O., & Ichimov, M. A. M. (2025). Developing a deep learning-based sentiment analysis system of hotel customer reviews for sustainable tourism. Sustainability, 17(13), 5756.
  • Gaur, L., Afaq, A., Solanki, A., Singh, G., Sharma, S., Jhanjhi, N., My, H. T., & Le, D.-N. (2021). Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels. Computers and Electrical Engineering, 95, 107374.
  • Gutierrez, E., Karwowski, W., Fiok, K., Davahli, M. R., Liciaga, T., & Ahram, T. (2021). Analysis of human behavior by mining textual data: Current research topics and analytical techniques. Symmetry, 13(7), 1276.
  • Islam, M. S., Kabir, M. N., Ghani, N. A., Zamli, K. Z., Zulkifli, N. S. A., Rahman, M. M., & Moni, M. A. (2024). Challenges and future in deep learning for sentiment analysis: A comprehensive review and a proposed novel hybrid approach. Artificial Intelligence Review, 57(3), 62.
  • Ku, C. H., Chang, Y.-C., Wang, Y., Chen, C.-H., & Hsiao, S.-H. (2019). Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
  • Kumas, G., Bilgili, B., & Avcıkurt, C. (2025). Fiziksel çevre açısından kamp–karavan turizmi: Sistematik derleme. Şura Akademi, 10, 33–50.
  • Kurnalı, M. (2024). Sürdürülebilir kentler için bir başlangıç noktası olarak net sıfır karbon küçük ev (Tiny House) köyleri. Kent Akademisi, 17(Sürdürülebilir İnsani Kalkınma ve Kent), 68–83.
  • Kumar, D., Gupta, A., Gupta, V. K., & Gupta, A. (2023). Aspect-based sentiment analysis using machine learning and deep learning approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5S), 118–138.
  • Mariani, M., & Borghi, M. (2021). Customers’ evaluation of mechanical artificial intelligence in hospitality services: A study using online reviews analytics. International Journal of Contemporary Hospitality Management, 33(11), 3956–3976.
  • Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: A thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77.
  • Mohammadi, E., & Karami, A. (2022). Exploring research trends in big data across disciplines: A text mining analysis. Journal of Information Science, 48(1), 44–56.
  • Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets, 31, 553–574.
  • Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81.
  • Naz, A., Khan, H. U., Bukhari, A., Alshemaimri, B., Daud, A., & Ramzan, M. (2025). Machine and deep learning for personality traits detection: A comprehensive survey and open research challenges. Artificial Intelligence Review, 58(8), 1–57.
  • OtelPuan. (2025). Read – Discover – Comment. https://www.otelpuan.com/
  • Pant, V. K., Sharma, R., & Kundu, S. (2024). An overview of stemming and lemmatization techniques. Advances in Networks, Intelligence and Computing, 308–321.
  • Patel, A., Shah, N., Parul, V. B., & Suthar, K. S. (2023). Hotel recommendation using feature and machine learning approaches: A review. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (ss. 1144–1149). IEEE.
  • Sharma, K., Trott, S., Sahadev, S., & Singh, R. (2023). Emotions and consumer behaviour: A review and research agenda. International Journal of Consumer Studies, 47(6), 2396–2416.
  • Sohel, A., Hossain, M. R., Mostofa, Z. B., Hasan, M. U., Das, U. C., & Parvin, S. K. (2023). Sentiment analysis based on online course feedback using TextBlob and machine learning techniques. In 2023 26th International Conference on Computer and Information Technology (ICCIT).
  • Sunar, H., Ateş, A., & Köseoğlu, A. (2025). Konaklama işletmelerinde yapay zekâ uygulamaları üzerine yapılan çalışmaların incelenmesi. Söke İşletme Fakültesi Dergisi, 2(3), 37–51.
  • Tahir, A. H., Adnan, M., & Saeed, Z. (2024). The impact of brand image on customer satisfaction and brand loyalty: A systematic literature review. Heliyon, 10(16), e36254.
  • Thakur, K., & Kumar, V. (2022). Application of text mining techniques on scholarly research articles: Methods and tools. New Review of Academic Librarianship, 28(3), 279–302.
  • Türkay, B. (2024). Sağlık turizmi işletmelerinde çevrimiçi müşteri şikâyetlerinin analizi ve hizmet kalitesi iyileştirmedeki rolü. İşletme Araştırmaları Dergisi, 16(4), 2365–2382.
  • Van Riel, C. B. (1997). Research in corporate communication: An overview of an emerging field. Management Communication Quarterly, 11(2), 288–309.
  • Vito, D. (2025). Corporate reputation as strategic intangible asset: An analysis of management processes, measurement methods and impact on bank and auditors’ decisions. Springer Nature.

Corporate Reputation Analysis of Customer Reviews for Tiny House Hotels with Artificial Intelligence

Yıl 2025, Cilt: 2 Sayı: 4, 19 - 34, 30.12.2025

Öz

This study analyses the corporate reputation of six different tiny house hotels operating in Turkey based on online customer reviews. Data was obtained from the OtelPuan platform, and hotel names were concealed and coded as TH1–TH6. Sentiment analysis was performed using the TextBlob library, and reviews were categorised as positive, negative, or neutral. The findings show that positive reviews predominate for all hotels, while negative reviews are limited. The TH3 hotel stands out, with 93% positive sentiment. Word frequency and word cloud analyses reveal that cleanliness, staff attentiveness, breakfast, and value for money are prominent in positive reviews, while location, room layout, and technical deficiencies are highlighted in negative reviews. Neutral reviews are mostly descriptive and explanatory in nature. In conclusion, Tiny House hotels are seen to have a strong corporate reputation in the digital environment.

Etik Beyan

There is no need to obtain ethical permission for the current study as per the legislation.

Destekleyen Kurum

TÜBİTAK

Proje Numarası

1919B012415353

Teşekkür

This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) Scientific Personnel Support Directorate (BİDEB) under the 2209-A University Students Research Projects Support Programme (Project Number: 1919B0124153.

Kaynakça

  • Akyol, D. P. (2022). Küçük ev (tiny house) olgusunun alternatif turizme yönelik geçici konaklama mekânı olarak potansiyellerinin değerlendirilmesi: Antalya/İbradı örneği (Yüksek lisans tezi). Bursa Uludağ Üniversitesi.
  • Alotaibi, E. (2020). Application of machine learning in the hotel industry: A critical review. Journal of Association of Arab Universities for Tourism and Hospitality, 18(3), 78–96.
  • Ashqar, R. I., & Ramos, C. M. (2023, Şubat). Machine-learning holistic review in tourism and hospitality. In The International Conference on Global Economic Revolutions (ss. 78–84). Springer Nature Switzerland.
  • Bashiri, H., & Naderi, H. (2024). Comprehensive review and comparative analysis of transformer models in sentiment analysis. Knowledge and Information Systems, 66(12), 7305–7361.
  • Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: Using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386–403.
  • Büyükeke, A. (2025). Dijital platformlarda çevrimiçi itibar yönetimi: Belek ve Kaş bölgelerindeki otellerin müşteri ilişkileri yönetimi stratejilerinin analizi. Sosyal Bilimler Araştırmaları Dergisi, 20(1), 114–143.
  • Chun, R. (2005). Corporate reputation: Meaning and measurement. International Journal of Management Reviews, 7(2), 91–109.
  • Çaylak, P. Ç., Kayakuş, M., Eksili, N., Yiğit Açikgöz, F., Coşkun, A. E., Ichimov, M. A. M., & Moiceanu, G. (2024). Analysing online reviews consumers’ experiences of mobile travel applications with sentiment analysis and topic modelling: The example of Booking and Expedia. Applied Sciences, 14(24), 11800.
  • Erdoğan, D., Kayakuş, M., Çelik Çaylak, P., Ekşili, N., Moiceanu, G., Kabas, O., & Ichimov, M. A. M. (2025). Developing a deep learning-based sentiment analysis system of hotel customer reviews for sustainable tourism. Sustainability, 17(13), 5756.
  • Gaur, L., Afaq, A., Solanki, A., Singh, G., Sharma, S., Jhanjhi, N., My, H. T., & Le, D.-N. (2021). Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels. Computers and Electrical Engineering, 95, 107374.
  • Gutierrez, E., Karwowski, W., Fiok, K., Davahli, M. R., Liciaga, T., & Ahram, T. (2021). Analysis of human behavior by mining textual data: Current research topics and analytical techniques. Symmetry, 13(7), 1276.
  • Islam, M. S., Kabir, M. N., Ghani, N. A., Zamli, K. Z., Zulkifli, N. S. A., Rahman, M. M., & Moni, M. A. (2024). Challenges and future in deep learning for sentiment analysis: A comprehensive review and a proposed novel hybrid approach. Artificial Intelligence Review, 57(3), 62.
  • Ku, C. H., Chang, Y.-C., Wang, Y., Chen, C.-H., & Hsiao, S.-H. (2019). Artificial intelligence and visual analytics: A deep-learning approach to analyze hotel reviews & responses. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
  • Kumas, G., Bilgili, B., & Avcıkurt, C. (2025). Fiziksel çevre açısından kamp–karavan turizmi: Sistematik derleme. Şura Akademi, 10, 33–50.
  • Kurnalı, M. (2024). Sürdürülebilir kentler için bir başlangıç noktası olarak net sıfır karbon küçük ev (Tiny House) köyleri. Kent Akademisi, 17(Sürdürülebilir İnsani Kalkınma ve Kent), 68–83.
  • Kumar, D., Gupta, A., Gupta, V. K., & Gupta, A. (2023). Aspect-based sentiment analysis using machine learning and deep learning approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5S), 118–138.
  • Mariani, M., & Borghi, M. (2021). Customers’ evaluation of mechanical artificial intelligence in hospitality services: A study using online reviews analytics. International Journal of Contemporary Hospitality Management, 33(11), 3956–3976.
  • Mehraliyev, F., Chan, I. C. C., & Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: A thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77.
  • Mohammadi, E., & Karami, A. (2022). Exploring research trends in big data across disciplines: A text mining analysis. Journal of Information Science, 48(1), 44–56.
  • Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets, 31, 553–574.
  • Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81.
  • Naz, A., Khan, H. U., Bukhari, A., Alshemaimri, B., Daud, A., & Ramzan, M. (2025). Machine and deep learning for personality traits detection: A comprehensive survey and open research challenges. Artificial Intelligence Review, 58(8), 1–57.
  • OtelPuan. (2025). Read – Discover – Comment. https://www.otelpuan.com/
  • Pant, V. K., Sharma, R., & Kundu, S. (2024). An overview of stemming and lemmatization techniques. Advances in Networks, Intelligence and Computing, 308–321.
  • Patel, A., Shah, N., Parul, V. B., & Suthar, K. S. (2023). Hotel recommendation using feature and machine learning approaches: A review. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (ss. 1144–1149). IEEE.
  • Sharma, K., Trott, S., Sahadev, S., & Singh, R. (2023). Emotions and consumer behaviour: A review and research agenda. International Journal of Consumer Studies, 47(6), 2396–2416.
  • Sohel, A., Hossain, M. R., Mostofa, Z. B., Hasan, M. U., Das, U. C., & Parvin, S. K. (2023). Sentiment analysis based on online course feedback using TextBlob and machine learning techniques. In 2023 26th International Conference on Computer and Information Technology (ICCIT).
  • Sunar, H., Ateş, A., & Köseoğlu, A. (2025). Konaklama işletmelerinde yapay zekâ uygulamaları üzerine yapılan çalışmaların incelenmesi. Söke İşletme Fakültesi Dergisi, 2(3), 37–51.
  • Tahir, A. H., Adnan, M., & Saeed, Z. (2024). The impact of brand image on customer satisfaction and brand loyalty: A systematic literature review. Heliyon, 10(16), e36254.
  • Thakur, K., & Kumar, V. (2022). Application of text mining techniques on scholarly research articles: Methods and tools. New Review of Academic Librarianship, 28(3), 279–302.
  • Türkay, B. (2024). Sağlık turizmi işletmelerinde çevrimiçi müşteri şikâyetlerinin analizi ve hizmet kalitesi iyileştirmedeki rolü. İşletme Araştırmaları Dergisi, 16(4), 2365–2382.
  • Van Riel, C. B. (1997). Research in corporate communication: An overview of an emerging field. Management Communication Quarterly, 11(2), 288–309.
  • Vito, D. (2025). Corporate reputation as strategic intangible asset: An analysis of management processes, measurement methods and impact on bank and auditors’ decisions. Springer Nature.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yönetim Bilişim Sistemleri
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Kayakuş 0000-0003-0394-5862

Derin Ceviz 0009-0003-6273-2645

Proje Numarası 1919B012415353
Gönderilme Tarihi 29 Ekim 2025
Kabul Tarihi 5 Aralık 2025
Yayımlanma Tarihi 30 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 2 Sayı: 4

Kaynak Göster

APA Kayakuş, M., & Ceviz, D. (2025). Tiny House Otellere Yönelik Müşteri Yorumlarının Yapay Zekâ ile Kurumsal İtibar Analizi. Söke İşletme Fakültesi Dergisi, 2(4), 19-34.