Amazon Recommendation Classification
Amazon curates the buying experience for each user utilizing advanced algorithms and frequent item-set techniques to drive revenue. In addition to recommendation algorithms, pessimistic or interested buyers will consult the reviews posted below a product to gauge whether the product is a “smart” purchase. Our goal is to accurately classify the review score as function of review summary and text.
We utilized NLP techniques, such as Non-Negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), and Term Frequency–Inverse Document Frequency (TF-IDF) to classify the Amazon reviews. We utilized the Ridge regression technique that exhibited 0.83963 Root Mean Squared Error (RMSE).
