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

Anber AbraheemShlash Mohammad, Ammar Mohammad Al-Ramadan, Suleiman Ibrahim Mohammad, Badrea Al Oraini, Asokan Vasudevan, Nawaf Alshdaifat and Mohammad Faleh Ahmmad Hunitie

Data and Metadata, 2025, vol. 4, 922

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.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:922:id:1056294dm2025922

DOI: 10.56294/dm2025922

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().