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Sentiment Analysis of Berlin Tourists’ Food Quality Perception Through Artificial Intelligence

Author

Listed:
  • Omid Shafiezad

    (Hochschule für Technik und Wirtschaft (HTW) Berlin, Treskowallee 8, 10318 Berlin, Germany)

  • Hamid Mostofi

    (Department of Work, Technology and Participation, Technische Universität Berlin, 10587 Berlin, Germany)

Abstract

This study examines how tourists perceive food quality in Berlin using AI-driven sentiment analysis tools. The goal is to understand the factors shaping tourists’ perceptions and provide insights to improve the hospitality industry and customer satisfaction. By analyzing reviews from online platforms, this research identifies key themes and trends in tourists’ feedback. The use of AI, specifically for sentiment analysis, supports efficient and detailed evaluation of customer opinions. This study employed lexicon-based sentiment analysis to evaluate tourists’ feedback on online platforms and compared the sentiment scores of textual feedback with their direct rating scores. The results show that integrating sentiment scores derived from AI tools with tourists’ rating scores provides deeper insights into service quality within the tourism sector.

Suggested Citation

  • Omid Shafiezad & Hamid Mostofi, 2024. "Sentiment Analysis of Berlin Tourists’ Food Quality Perception Through Artificial Intelligence," Tourism and Hospitality, MDPI, vol. 5(4), pages 1-22, December.
    • Handle: RePEc:gam:jtourh:v:5:y:2024:i:4:p:78-1417:d:1540043
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    References listed on IDEAS

    as
    1. Marina Paolanti & Adriano Mancini & Emanuele Frontoni & Andrea Felicetti & Luca Marinelli & Ernesto Marcheggiani & Roberto Pierdicca, 2021. "Tourism destination management using sentiment analysis and geo-location information: a deep learning approach," Information Technology & Tourism, Springer, vol. 23(2), pages 241-264, June.
    2. Berkalp Tunca & Sinan Saraçlı, 2021. "Artificial Neural Network approach on Type II Regression Analysis," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 9(2), pages 247-258, December.
    Full references (including those not matched with items on IDEAS)

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