Aspect-Based Sentiment Analysis of Amazon Product Reviews Using Machine Learning Models and Hybrid Feature Engineering

While sentiment analysis is a popular and significant research trend, aspect-based sentiment analysis (ABSA) requires more focus from researchers. The customer reviews of headphones and Bluetooth devices on Amazon are the main subject of this study. Several machine learning (ML) algorithms are used in the study, including Support Vector Machine (SVM), k-nearest Neighbors (KNN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR). Additionally, a hybrid feature engineering technique combining TF-IDF (Term Frequency-Inverse Document Frequency) and word n-gram is applied, specifically utilizing word n-gram (1,4) in conjunction with TF-IDF. The results of evaluating these methods showed that, with an accuracy of 91%, SVM with hybrid word n-gram (1,3) produced the best outcomes. The research dataset exhibits imbalance, which is addressed by using the Matthews Correlation Coefficient (MCC) as an additional performance metric. This results in a score of 0.77. The results show that aspect-based sentiment analysis is effective in gaining insightful information from customer reviews of headphones and Bluetooth devices on Amazon. The SVM algorithm and the designated hybrid feature engineering technique perform better than the other.