Analysing customers' reviews and ratings for online food deliveries: A text mining approach

Abstract

AbstractThe purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. An Indian food delivery portal (Zomato.com) was used, where 50 restaurants on Presence Across Nation (PAN) basis were selected through stratified random sampling. A total of 2530 reviews were collected, scrutinized, and analysed. Using the NVivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub‐themes. Linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub‐themes. Regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. These variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. Similarly, negative sentiments were observed in pricing and hygiene sub‐themes. Restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. Similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost‐prohibitive. This study devised a standardized methodology for analysing vast amounts of online user‐generated content (UGC). Findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.