This is like having a robot read thousands of customer reviews and convert all the feelings (happy, angry, neutral) into a clear product score so you instantly see if people love or hate a product.
Manually reading and interpreting large volumes of product reviews to understand customer satisfaction is slow, inconsistent, and does not scale; this system automatically converts customer sentiment into an overall product rating.
Access to large volumes of domain-specific customer reviews and integration into existing product feedback and rating workflows can create a data and workflow moat; algorithmically this is relatively easy to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Label quality and domain transferability of the sentiment model; maintaining accuracy across languages, slang, and evolving review patterns.
Early Majority
Focus on turning unstructured sentiment from text reviews directly into a quantitative product rating, rather than just tagging sentiment, making it easier to plug into dashboards and decision processes.