Abstract:
E-commerce product reviews are vitally important for maintaining a positive
reputation and for increasing sales opportunities. Generally, there are thousands of
reviews available for a product and therefore, it is time consuming to analyze reviews
to get the idea of the product manually. This study is focused on evaluating the quality
of e-commerce products by analyzing reviews gathered from AliExpress online
marketing. In this analysis, a security camera product was selected, and 568 textual
reviews were collected for this product. Then, after preprocessing, this dataset was
divided into a training (80%) set and a testing (20%) set for analysis. Reviews in
English language were selected for this research and therefore non-textual elements
such as emojis were disregarded. To address potential imbalances in the dataset,
particularly in instances of class imbalance, the Synthetic Minority Over-Sampling
Technique (SMOTE) was employed. This technique helped to ensure a more
equitable representation of different classes within the dataset. After that, binary
vectorization method was used to transform the text data into numerical vectors,
facilitating the application of machine learning techniques. The Support Vector
Machine (SVM) served as the chosen machine learning model for this analysis,
achieving a remarkable training accuracy of 95.8% and a testing accuracy of 86% of
the prediction model. Then a prediction pipeline designed method was used to
classify the reviews as either "positive" or "negative," assessing the quality based on
the customer feedback. The results of this analysis are presented in graphical and
tabular formats, aiding potential consumers in making informed purchasing
decisions. This model helps consumers to quickly see the quality of the product, as it
reviews all the data providing a predictive model for product quality. Despite
limitations, our approach offers valuable insights for consumers navigated online
marketplaces, aiding them in evaluating the desirability and worthiness of products
under consideration.
