Paper Title
Harnessing Ml Techniques in Online Food Delivery Platforms Using Sentiment Based Recommendations

Abstract
The recent digital revolution happened due to the COVID-19 pandemic, and rapid urbanisation has aided in the expansion of online food services globally. Food delivery apps have become a huge hit among well-informed people all over the world, and online ordering has swept the food industry. FDS organisations aim to gather complaints from customer feedback and efficiently use the data to find areas for improvement to enhance customer satisfaction. Customer feedback is the main source of information for both customers and decision-makers in these services, and it helps in addressing negative reviews and finding the root causes leading to customer satisfaction. For any business, customer management is important, and hence it can be done using AI techniques and tools efficiently. The sentimental analysis is being implemented using machine learning (ML) algorithms to provide trust and interpretability in the model, provide organisations with reliable insights for navigating the complexities in the online food industry, and enhance customer satisfaction. Therefore, we believe that our proposed model would bring a significant change to the online food industry. Keywords - Online Food Delivery (OFD), Sentimental Analysis, Customer Feedback, Customer Management, Machine Learning