Sentiment prediction based on analysis of customers assessments in food serving businesses

Human activities and behaviour in different domains are usually influenced by other people's actions and opinion. Nowadays, it is evident that there is a growing research interest in sentiment analysis, evaluation and prediction. Content from web sources and social media is frequently used when people want to see others' opinion about different things. Our research is focused on ML-based sentiment analysis of food services reviews data. The comparison of several regression models with regards to prediction of customer satisfaction of restaurant and food services is presented. The experimental data collected from food serving businesses located in Shanghai Lujiazui Commercial Zone includes keywords extracted from the customers' written reviews. Additionally, the data are spatially labelled enabling to conduct separate analyses for different geographical regions. As a conclusion, the keywords extracted from the customer's reviews were suitable for the prediction of three observed satisfaction criteria: food taste, service, and environment.

Automated summary

This research focuses on ML-based sentiment analysis of food services reviews, comparing regression models to predict customer satisfaction. Keywords extracted from customer reviews have potential for predicting satisfaction in food taste, service, and environment aspects.