Sentiment analysis of customer reviews on food ordering portals of Bangladesh using natural language processing

Abstract

In recent years, online food ordering services have gained popularity by providing customers with suitable and user-friendly platforms for ordering food from restaurants and receiving doorstep delivery. Foodpanda Bangladesh and HungryNaki have been anticipated to make significant contributions to the expansion and development of the online food delivery market during this period. This study aims to forecast the attitudes of Bangladeshi consumers toward digital platforms for food ordering, with a particular focus on Foodpanda Bangladesh and HungryNaki. To achieve this goal, an online review sentiment analysis will be implemented. A dataset of customer reviews from the company’s website will be compiled. The data will undergo preprocessing techniques to filter out unnecessary and irrelevant information and refine the features and characteristics of the data. Subsequently, natural language processing (NLP) techniques will be applied to conduct sentiment analysis. The objective of this research is to determine the prevailing customer opinions regarding restaurants and food delivery platforms in Bangladesh. This includes their future assessments of delivery schedules, meal quality, and customer service on the platform. The results of this research should shed light on the future of Bangladesh’s food-ordering portals from the perspective of their users. The research will help the platform enhance its reputation and competitiveness in the online food delivery market.