Precision Ranking: Harnessing NLP and Normalization for Enhanced Rating and Review Analysis

This research paper aims to introduce a mess menu voting system as a solution to the problem of meeting the demands of a varied student body while reducing food waste in university dining halls. This system uses real-time feedback methods to gather user ratings and reviews for every menu item, in contrast to traditional research initiatives. Users can precisely describe their dietary preferences and constraints by using an easy-to-use web platform, which facilitates tailored menu selection. Our research delves into the complex workings of this system, highlighting its effectiveness in optimizing menu offerings, cutting down on waste, and enhancing the eating experience. The Mess Menu Voting System empowers consumers and enables data-driven decision-making by utilizing sophisticated analytical techniques like weight computation, normalization, sentiment analysis, and standard deviation calculation. A new age in institutional dining is being ushered in by this cutting-edge technology, which provides a more engaging, sustainable, and pleasurable meal experience for all parties involved.