Mood-Based Food Recommendation System

This paper presents a web-based food recommendation system that personalizes dining suggestions based on the user’s mood, integrating emotional well-being with convenience and health consciousness. Utilizing natural language processing techniques on comfort food datasets, the system identifies mood-specific comfort foods and recommends nearby restaurants serving corresponding cuisines. Additionally, it provides nutritional information and healthier alternatives to promote informed dietary choices. The platform supports two user roles—admins and users—with features including user management, health tips, and query handling to enhance interaction and support. Implemented in Python using libraries such as Pandas and NLTK, the system employs text preprocessing and frequency analysis to generate accurate, mood-aligned recommendations. This approach aims to improve customer satisfaction, foster emotional connection with food, and differentiate food delivery services in a competitive market by combining personalized mood-based food suggestions with health guidance and responsive user engagement.