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Product Reviews Analysis

Goal:

To understand the following about Product Reviews - what are most frequently used words, distribution of the length of the reviews, understanding fake reviews, spelling errors, predicting product rating using the reviews, etc.

Data Description:

• [kaggle dataset] (https://www.kaggle.com/datafiniti/grammar-and-online-product-reviews)

• 71000+ reviews for 1000 products

Features

  • id
  • reviewsText
  • reviewTitle
  • reviewRating
  • purchasedProduct
  • recommendProduct
  • brand / manufacturer
  • categories
  • pack_size
  • descriptions
  • dateAdded

WORKFLOW

  • EDA (data cleaning)
  • NLP (tokenization, removing stop words, stemming)
  • TOPIC MODELING (NMF with TF-IDF Vectorizer)
  • RECOMMENDATION SYSTEM (content based filtering using the terms from reviews using cosine similarity matrix)
  • CLASSIFICATION SYSTEM (Logostic Regression and Decision Tree Classifier)

topics

Recommendation System example -

Input - 'skin cream'

Recommendations:

  • 'Olay Total Effects Daily Moisturizer
  • '7-In-1 Anti-Aging, 0.5oz'
  • 'Olay Regenerist Deep Hydration Regenerating Cream'
  • "L'oreal Paris Revitalift Triple Power Deep-Acting Moisturizer"
  • 'CeraVe SA Renewing Cream'
  • 'Shea Moisture African Black Soap Problem Skin Facial Mask'
  • 'Tree Hut Shea Body Butters, Coconut Lime, 7 oz'
  • 'Nivea Extended Moisture Body Lotion

I have done Dimensionality Reduction using PCA, and also tried K-Means clustering.