Customer Sentiment Analysis for Food and Beverage Development in Restaurants using AI in Jordan

Authors

  • Anber AbraheemShlash Mohammad Digital Marketing Department, Faculty of Administrative and Financial Sciences, Petra University, Amman 11623, Jordan Author https://orcid.org/0000-0003-3513-3965
  • Ammar Mohammad Al-Ramadan Faculty of Hospitality and Tourism Management. Al-Ahliyya Amman University – Jordan Author https://orcid.org/0000-0002-8281-3384
  • Suleiman Ibrahim Mohammad Electronic Marketing and Social Media, Economic and Administrative Sciences Zarqa University, Jordan Author https://orcid.org/0000-0001-6156-9063
  • Badrea Al Oraini Department of Business Administration. Collage of Businesss and Economics, Qassim University, Qassim – Saudi Arabia Author https://orcid.org/0009-0009-3549-8172
  • Asokan Vasudevan Faculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia Author https://orcid.org/0000-0002-9866-4045
  • Nawaf Alshdaifat Faculty of Information Technology, Applied Science Private University, Amman, Jordan Author
  • Mohammad Faleh Ahmmad Hunitie Department of Public Administration, School of Business, University of Jordan, Jordan Author

DOI:

https://doi.org/10.56294/dm2025922

Keywords:

Customer Sentiment Analysis, Food and Beverage Industry, Hospitality Industry, Online Reviews

Abstract

Introduction: customer sentiment analysis is a vital tool for understanding consumer preferences and enhancing service quality in the food and beverage industry. Online reviews significantly influence customer decisions, making it essential for businesses to analyze sentiment trends and manage their digital reputation effectively. This study examines customer sentiment across different establishment types and digital platforms in Jordan, providing insights into sentiment patterns and their strategic implications.
Method: a dataset of 384 customer reviews from various restaurants and hotels was analyzed using a rule-based sentiment classification approach. Sentiments were categorized as positive, neutral, or negative. To assess sentiment variations, an ANOVA test was conducted to compare establishment types, and a Chi-Square test was performed to examine differences across digital platforms.
Results: findings indicate that luxury hotels and fine dining establishments receive more positive sentiment, while budget hotels and fast food chains experience higher negative sentiment. However, the ANOVA test showed no statistically significant sentiment differences across establishment types, suggesting that all businesses receive a mix of sentiment categories. The Chi-Square test confirmed significant sentiment differences across platforms, with TripAdvisor attracting the most positive reviews, Facebook and Google Reviews showing balanced sentiment, and Twitter experiencing the highest negative sentiment.
Conclusion: these findings emphasize the importance of platform-specific digital reputation management. Businesses should strategically engage with customers on different platforms, address complaints proactively, and utilize AI-driven sentiment analysis tools to improve customer satisfaction. Future research should explore AI-based predictive analytics and sentiment monitoring for enhancing service quality in the hospitality industry.

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2025-04-20

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Vol. 4 (2025): Data and Metadata

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Copyright (c) 2025 Anber AbraheemShlash Mohammad, Ammar Mohammad Al-Ramadan, Suleiman Ibrahim Mohammad, Badrea Al Oraini, Asokan Vasudevan, Nawaf Alshdaifat, Mohammad Faleh Ahmmad Hunitie (Author)

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Mohammad AA, Al-Ramadan AM, Mohammad SI, Al Oraini B, Vasudevan A, Alshdaifat N, et al. Customer Sentiment Analysis for Food and Beverage Development in Restaurants using AI in Jordan. Data and Metadata [Internet]. 2025 Apr. 20 [cited 2025 Nov. 30];4:922. Available from: https://dm.ageditor.ar/index.php/dm/article/view/922