The 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.

中文翻译：

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分析客户对在线食品配送的评论和评级：一种文本挖掘方法

本研究的目的是通过文本挖掘和实证技术探索在线评论和评分之间的关​​系。使用了印度食品配送门户网站 (Zomato.com)，通过分层随机抽样选择了 50 家基于全国分布 (PAN) 的餐厅。总共收集、审查和分析了 2530 条评论。使用 NVivo 软件进行定性分析，从收集到的评论中确定了七个主题，其中进一步探索了“交付”主题以识别子主题。线性回归模型用于识别影响交付评级的变量，并且还对识别的子主题进行了情绪分析。回归结果显示，卫生和定价（交付子主题）表现出较低的交付评级。这些变量可以作为餐馆和相关在线食品配送服务的指标，以围绕它们建立业务模型。同样，在定价和卫生子主题中也观察到了负面情绪。餐馆和在线食品服务可以提高其食品配送过程的卫生水平，以获得更高的配送评级。类似地，可以修改食品的价格，这样顾客就不会被阻止订购食品——食品和订购服务不会变得成本过高。本研究设计了一种用于分析大量在线用户生成内容 (UGC) 的标准化方法。这项研究的结果可以外推到其他部门和服务行业，例如旅游、清洁、运输、医院和工程，尤其是在大流行期间。