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Problem description

  • E-Commerce applications provide an added advantage to customers to buy a product with added suggestions in the form of reviews.

  • Obviously, reviews are useful and impactful for customers who are going to buy the products.

  • But these enormous amounts of reviews also create problems for customers as they are not able to segregate useful ones.

  • Regardless, these immense proportions of reviews make an issue for customers as it becomes very difficult to filter informative reviews.

Problem description from data science perspective

  • To develop a solution for this problem of ranking reviews I have been using a pairwise ranking approach that ranks reviews based on their relevance with the product and rank down irrelevant reviews.

Solution description

  • I developed the solution to this problem in four phases:

    1. Data preprocessing (which includes Language Detection, Gibberish Detection, Profanity Detection)

    2. Feature extraction

    3. Pairwise review ranking

    4. Classification

  • Outcome of the model is a list of reviews for a particular product ranking on the basis of relevance using a pairwise ranking approach.