E-Commerce Review Sentiment Classification and Prediction Using Machine Learning
Keywords:
Convolutional Neural Network (CNN), Customer reviews, Deep learning, E-commerce, Machine learning, Natural Language Processing (NLP), Sentiment analysisAbstract
The quantity of evaluations that have been produced has greatly increased as a result of the quick expansion of online commerce. As a consequence of this, computerised analysis of customer feedback is becoming an increasingly important component for both businesses and potential customers. This article provides a system that is based on natural language processing, convolutional neural networks, and support vector machines. The system is intended to be used for the purpose of e-commerce review sentiment categorisation and prediction. The management of missing values, the cleaning of text, the removal of punctuation and stop words, stemming, and label encoding are all procedures that are included in the pre-processing of customer review data. Following the completion of the processing step, the textual data are converted into numerical feature representations, which are then separated into two datasets: one for training purposes and another for testing purposes. Both CNN and SVM algorithms are able to identify customer reviews by analysing the polarity of the sentiments expressed in those reviews. There are several measures that are utilised in the process of evaluating the proposed models. These metrics include accuracy, precision, recall, and F1-score. To illustrate that machine learning and deep learning are capable of automatically classifying customer evaluations, the CNN model attained an accuracy of 96%, while the SVM model achieved 93% accurate classifications. This was done in order to demonstrate that these two types of learning are capable of doing so. The framework that has been proposed can be utilised by companies that are involved in e-commerce in order to successfully interpret enormous volumes of customer feedback, determine the feelings of consumers, and encourage data-driven customer experience and decision-making. This is achievable since the framework has been proposed.
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