ARCHIVES

The rapid growth of e-commerce platforms has resulted in an enormous volume of user-generated textual data in the form of customer reviews. These reviews play a crucial role in influencing purchasing decisions and provide valuable insights into product quality and customer satisfaction. However, analyzing such large-scale textual data manually is inefficient and time-consuming. Sentiment Analysis, a subfield of Natural Language Processing (NLP), offers automated techniques to classify opinions expressed in text into categories such as positive, negative, and neutral. This research proposes a robust sentiment analysis framework using a balanced dataset derived from the Amazon Fine Food Reviews corpus. One of the major challenges in sentiment classification is class imbalance, where positive reviews dominate the dataset, leading to biased predictions. To address this issue, a balanced dataset consisting of 150,000 reviews equally distributed across sentiment classes is constructed using an under-sampling approach. The proposed system integrates advanced NLP preprocessing techniques, including tokenization, stop-word removal, and stemming, followed by feature extraction using word embedding’s. A Long Short-Term Memory (LSTM) deep learning model is employed to capture contextual dependencies within textual data. The model is trained and evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed model achieves a test accuracy of 94.37%, indicating a significant improvement over traditional and imbalanced learning approaches. The use of a balanced dataset effectively reduces model bias and enhances classification reliability across all sentiment classes. The proposed framework can be effectively applied in real-world applications such as customer feedback analysis, product evaluation, and decision support systems.