On Amazon, buyers can submit reviews on products they have purchased. These reviews contribute to a potential buyer’s decision-making process, as buyers read reviews to decide whether to buy a product. Additionally, sellers depend on reviews to improve their product offerings. Amazon’s summary of reviews does not clearly indicate if an aspect of a product is mentioned positively or negatively. Buyers can manually read a small number of reviews to understand the overall sentiment towards a product, but reading reviews becomes progressively more difficult as the number of reviews increases, as it can lead to information overload. To address this problem, a hybrid machine learning classification algorithm that employs a branch of natural language processing, specifically aspect-based sentiment analysis, was developed to detect the polarity and key aspects mentioned in Amazon product reviews. Naïve Bayes, SVM, Decision Tree and Random Forest were compared to determine the two best algorithms for this purpose. The hybrid algorithm, named Soft Voting Hybrid Algorithm (SVHA), was implemented by training and testing a voting classifier using soft voting, which produced the final prediction by selecting the class with the highest average sum of probabilities from two base classifiers with the highest accuracies and macro F1-scores. Based on the experiments conducted, SVHA attained higher accuracies and macro F1-scores compared to the other four algorithms, showing its suitability in conducting aspect-based sentiment analysis.

Sentiment analysis, E-commerce, Natural language processing, Machine learning, Product reviews