An Integrated Approach for Amazon Electronic Products Reviews by Using Sentiment Analysis

Abstract

In our routine life, we interact a wide range of products, and frequently browse through digital media platforms to access their quality. Although the accessibility of online platforms, consumers often find it challenging to swiftly judge the quality of products on the basis of customer reviews. To cope this situation, the study addresses this problem by suggesting a machine learning-based solution to categorize product reviews. For this, we employ various machine learning techniques, including Random Forest, Naïve Bayes, Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) Classifier, and Bidirectional Encoder Representations from Transformers (BERT). In our model, we incorporate pre-processing methods for prepare the dataset for training and utilize feature extraction techniques such as TF-IDF and word2vec which are then applied to different classifiers to analyze the reviews. Moreover, we conduct this study by using the Amazon Electronics category dataset, it reveals that BERT outperforms other classifiers with a performance score of 0.8896. Therefore, this technique not only streamlines the procedure of evaluating product quality but also enhances the accuracy of review classification, giving a real-world solution for consumers and businesses alike.