Feature-Level Rating System Using Customer Reviews and Voting Metrics

The feature-level rating system proposed in this project analyzes customer reviews and review votes using sentiment analysis and Natural Language Processing (NLP). Based on user feedback, the system uses sentiment analysis to create ratings for each feature after extracting product features using a TextRank algorithm. For e-commerce platforms, this methodology generates a more detailed and trustworthy rating system by examining review votes together with textual sentiment. The framework offers a more sophisticated perspective on product features that are advantageous to both consumers and businesses, enhancing the legitimacy and utility of online ratings.