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🎯 BRIEF
The project focuses on categorizing and analyzing restaurant reviews using Latent Dirichlet Allocation (LDA) for topic modeling. By extracting underlying themes, the aim is to provide actionable insights for restaurants to enhance customer experience, self-evaluate, and stay competitive. This approach not only improves overall restaurant performance but also enables personalized recommendations for customers, contributing to a more satisfying dining experience.
🔧 TOOLS
Python, pandas, gensim, nltk, numpy, pyLDAvis, seaborn, Matplotlib
🤝 CONTRIBUTION
Applied various NLP techiniques to convert each review into its vector representation.
Tuned the LDA model to find the right number of topics using coherence score and perpelexity.
Utilized matric factorization to build a recommender system, suggesting restaurants based on users' preference.
🛠️ LIMITATIONS
Topics may not always represent the underlying meaning as the solution is not taking into account the contextual meaning of the words.
Interpretation of topic is subjective.
📷 SCREENSHOTS
Topics Classification
Recommender System
Google Sites
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