The Use of Machine Learning Algorithms in the Analysis of Sentiments of E- Commerce Customer Reviews and Recommendations Feedback

The aim of this research is to examine the use of machine learning models in the context of e-commerce customer reviews’ analysis, and more specifically, to classify customers’ recommendations based on textual feedback. The accumulation of a huge amount of unstructured big data reviews on the e-commerce platforms has a major drawback concerning the proper interpretation of the analyzed data, particularly in terms of the identification of overall customer sentiments. In the present study, we used a dataset of women’s clothing reviews and five classification algorithms, namely logistic regression, support vector machine, Naive Bayes, random forest, and light gradient boosting machine, and assessed their performance based on accuracy, precision, recall, and F1 score. The results show that the support vector machine model had the highest overall performance with 89.06% for accuracy and 90.49% for precision can be recommended for sentiment analysis with balanced performance. As for the results, logistic regression and light gradient boosting machine were also quite stable, especially in terms of precision and recall, while Naive Bayes and random forest were characterized by high recall and are good in identifying positive sentiment but with certain trade-offs in precision. The findings of the study are then compared with the previous literature for similarities and differences, especially with ensemble methods, such as random forest that had a fluctuating performance. The study finds that one model does not outperform the others, and the selection of the machine learning algorithm should be based on the characteristics of the dataset and the purpose of the analysis. Further studies are suggested to examine the utilization of deep learning models, the effect of elaborate preprocessing of data, and the concept of combining different models in order to improve the performance of sentiment analysis in the context of e-commerce.