A NOVEL CUSTOMER REVIEW ANALYSIS SYSTEM BASED ON BALANCED DEEP REVIEW AND RATING DIFFERENCES IN USER PREFERENCE

Abstract

The rapid growth of mobile applications and online e-commerce platforms has made it increasingly easy to gather large amounts of data, providing valuable insights into consumer behavior. Analyzing user reviews has become essential in assisting users with purchasing decisions. In the proposed system, we introduce a solution by combining NLP (Natural Language Processing) techniques with a CNN (Convolutional Neural Network) model for review classification. The model incorporates text preprocessing, tokenization, and word embedding techniques to better understand the nuances of review content. The CNN-based architecture enhances the ability to detect meaningful patterns and relationships in the data, significantly improving prediction accuracy and computational efficiency. This approach overcomes the limitations of previous methods by providing a more accurate and scalable model for review analysis. It can be easily adapted to handle large-scale datasets and diverse textual data. Through experimental evaluation, the proposed system demonstrates superior performance, showing better classification results compared to existing approaches. By focusing on key patterns and relationships within the text data, the system offers an efficient and effective solution for predicting helpful reviews and enhancing decisionmaking confidence in e-commerce platforms.