Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques

Abstract

In the fiercely competitive retail industry, satisfying consumer expectations while optimizingcompany processes is more important than ever. Therefore, it is crucial to handle and channel datain a way that both seeks to delight consumers and generates healthy revenues if you want tosurvive and prosper. Data—or more specifically, Big data analytics is being utilized by largeretailers at every stage of the process, participants in the global and Indian retail markets,including tracking new, popular items and predicting sales. The use of machine learningclassification approaches for sentiment analysis in online shopping is examined in this research,utilizing a publicly available Amazon review dataset. The text-cleaning techniques processed thedataset before converting texts into numerical representations by implementing TF-IDF measures.The assessment concentrated on the three machine learning models' F1-score, accuracy, andprecision-recall: Bidirectional Encoder Representations from Transformers (BERT), SupportVector Machine (SVM), and Gradient Boosting (GB). BERT ended up outperforming all othermodels by demonstrating 89% accuracy, which proves its extraordinary capability to detectcustomer sentiments. The research results show how transformer-based models work forimproving sentiment analysis procedures in marketing analytics applications.