Customer reviews analytics on food delivery services in social media: a review

Noor Sakinah Shaeeali, Azlinah Mohamed, Sofianita Mutalib


Food delivery services have gained attention and become a top priority in developed cities by reducing travel time and waiting time by offering online food delivery options for a variety of dishes from a wide variety of restaurants. Therefore, customer analytics have been considered in business analysis by enabling businesses to collect and analyse customer feedback to make business decisions to be more advanced in the future. This paper aims to study the techniques used in customer analytics for food delivery services and identify the factors of customers’ reviews for food delivery services especially in social media. A total of 53 papers reviewed, several techniques and algorithms on customer analytics for food delivery services in social media are Lexicon, machine learning, natural language processing (NLP), support vector machine (SVM), and text mining. The paper further analyse the challenges and factors that give impacts to the customers’ reviews for food delivery services. These findings would be appropriate for development and enhancement of food delivery services in future works.


Artificial intelligence, Food delivery services, Sentiment Analysis, Social media

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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