Feature-Level Rating System using Customer Reviews and Review Votes

bstract: The rapid growth of online reviews has made customer feedback a critical source of insight for businesses. However, traditional rating systems often offer limited value by providing only an overall score without breaking down individual aspects or features of a product or service. This project proposes a Feature-Level Rating System that uses customer review votes combined with machine learning (ML) and data analysis techniques to deliver more detailed and accurate feature-specific ratings. The proposed system begins by processing large volumes of customer reviews using Natural Language Processing (NLP) techniques to identify key product features mentioned in the feedback. Text classification algorithms, such as Support Vector Machines (SVM) or neural networks, classify the reviews based on feature-specific categories (e.g., battery life, usability, design, etc.). Next, sentiment analysis models, including tools like VADER or Long Short-Term Memory (LSTM) networks, assess the sentiment of each review with respect to specific features. This allows the system to determine whether a customer’s opinion is positive, negative, or neutral for each feature. Finally, the system employs machine learning regression models (e.g., Random Forest or Gradient Boosting) to aggregate sentiment scores and calculate individual ratings for each feature, giving a granular view of customer satisfaction. By doing so, businesses gain a deeper understanding of which specific aspects of a product or service are performing well and which require improvement. This feature-level insight provides more actionable data for product development and enhances the customer decision-making process, offering a valuable tool for businesses to better align their offerings with customer expectations.