E-Commerce

Generate Star Ratings from User Reviews

Turn user reviews from text to discrete categories in order to make them sortable, calculate averages or improve existing rating categories.

Description

User reviews have become a driving force in e-commerce transactions. However, these reviews are often not as clear as they appear to be and it is especially hard to categorize them. AI sentiment analysis can help to classify text reviews based on their underlying sentiment. This classification can be expressed as, for example, a star rating schema which allows easier categorization or review summaries.

Who is this use case for?

Product managers of e-commerce sites or web pages that collect user feedback or reviews in textual form.

Business Goal

Support managers  to

  • make better informed decisions based on their total average users ratings
  • filter user ratings for very good or very bad reviews.

Preconditions

User reviews are stored in tabular form, one review per line either with or without existing rating category.

User review table before use case.

Triggers

The use case is triggered every time a user submits a new review.

How it works

  1. New user review is stored in database, with or without explicit rating
  2. Text review is sent to AI service for sentiment analysis
  3. Sentiment score is stored for the user review
  4. Sentiment score is mapped to star rating schema based on custom business logic

Postconditions (Outcome)

Each user review now has a sentiment score and a mapped star rating value.

User review table after use case

This new information can now be used to:

  • Calculate summary statistics over all user ratings
  • Compare the calculated star rating to the user rating and suggest corrections
  • Show very low  (negative) or very high (positive)  reviews to the manager
  • Impute a star rating where you don't have it yet

Impact

  • Make better decisions from your user reviews
  • Identify products that customers truly love or hate and why
  • Suggest ratings to users based on their review text

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