Enhanced E-commerce decision-making through sentiment analysis using machine learning-based approaches and IoT.
Journal:
PloS one
Published Date:
Jun 30, 2025
Abstract
E-commerce is a vital component of the world economy, providing people with a simple and convenient method for shopping and enabling businesses to expand into new global markets. Improving e-commerce decision-making by utilizing IoT and machine intelligence represents an important area for the impact of these technologies. Our objective is to elevate online shopping to a new level, making it a practical and genuinely delightful experience for customers. Businesses can acquire valuable insights to improve their operations and sales strategies by employing IoT devices to collect customer behavior and preference data and using machine learning (ML) algorithms to analyze them. In addition, companies can make simple recommendations using machine learning on the collected data. Our creative implementation of ML algorithms extends beyond simple recommendations. It also includes demand forecasting, guaranteeing that popular products are constantly in stock, reducing disappointments, and increasing consumer satisfaction. We applied several ML techniques, including logistic regression, Naïve Bayes, Support Vector Machine (SVM), Random Forest (RF), AdaBoosting, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). AdaBoosting outperformed the deep learning (DL) techniques LSTM and GRU and four ML techniques, logistic regression, Naïve Bayes, SVM, and RF, regarding F1 scores, accuracy, precision, and recall. It achieved an accuracy of 88%, an F1-score of 0.927, precision-1 of 0.908, and the ability of identifying true negatives and true positives (recall-0 and recall-1) of 0.569 and 0.947 respectively. Except for SVM, the other ML techniques did not exhibit much performance difference when using the count vectorizer and TD-IDF vectorizer. This study advances e-commerce capabilities through IoT and machine learning and paves the way for a new era of customer-centric, efficient, and adaptive retail strategies.
