An SBERT and DNN-Enhanced Boosted Rules Classifier Framework for Accurate Multi-Class Sentiment Analysis of Product Reviews

Abstract

The global e-commerce market is projected to surpass USD 7 trillion by 2030, with more than 80% of consumers relying heavily on product reviews to inform their purchasing decisions. Despite this reliance, manual sentiment analysis of such reviews is inherently limited in scalability and consistency, often leading to delayed insights and subjective or inaccurate interpretations. To overcome these limitations, this study introduces a comprehensive Natural Language Processing (NLP) framework designed for automated sentiment classification using a labeled product review dataset. The proposed approach begins with extensive preprocessing and Exploratory Data Analysis (EDA) to clean, standardize, and analyze the underlying data distribution. For advanced semantic representation, Sentence Bidirectional Encoder Representations from Transformers (SBERT) is utilized to generate context-rich embeddings, enabling the capture of nuanced linguistic patterns beyond the capabilities of traditional feature extraction methods. To address the issue of class imbalance across sentiment categories, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to create synthetic samples, thereby ensuring balanced and effective model training. In contrast to conventional machine learning models such as Random Forest Classifier (RFC), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost), the proposed framework integrates a Deep Neural Network (DNN)-based feature selection mechanism with a Boosted Rules Classifier (BRC). This hybrid architecture not only enhances predictive accuracy but also improves model interpretability. The system categorizes customer sentiments into three distinct classes: Negative, Neutral, and Positive. Experimental findings demonstrate that the proposed approach achieves higher classification accuracy with reduced bias, validating its effectiveness. The framework offers a scalable and reliable solution for sentiment analysis, empowering businesses to make data-driven decisions in areas such as product development, marketing strategy optimization, and customer relationship management (CRM), ultimately enabling more responsive and customercentric operations.