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

Md Abubakkar

Abstract:

While sentiment analysis is a popular and signif-
icant 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.