Welcome to our innovative project that revolutionizes restaurant menu optimization through the power of sentiment analysis derived from customer reviews. This repository showcases our approach to using cutting-edge Natural Language Processing (NLP) techniques, leveraging renowned frameworks such as NLTK, RoBERTa, SpaCy, and Word2Vec, to gain deep insights from Yelp reviews.
In today's competitive restaurant industry, understanding customer preferences and enhancing the dining experience is crucial. Our project combines the art of culinary expertise with computational tools to achieve just that. Here's what our project entails:
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Sentiment Analysis: We employ advanced NLP models like RoBERTa and NLTK to analyze and interpret customer feedback from Yelp reviews. This allows us to understand the sentiment behind each review and gain valuable insights.
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Menu Enhancement: By analyzing customer sentiment and reviews, we aim to identify popular dishes and areas for menu improvement with Word2Vec. This information empowers restaurant owners and chefs to align their offerings with customer preferences, ensuring a more satisfying dining experience.
- Sentiment Analysis with RoBERTa and NLTK
- Extraction of Food-Related Keywords using SpaCy
- Menu Optimization Recommendations (Word2Vec)
- Streamlit App
- 6,990,280 reviews
- 150,346 businesses
- 200,100 pictures
- 11 metropolitan areas