+44 330 818 7254 Research Article
Empowering Sentiment Analysis for English-Language Customer Reviews in E-commerce: A Comparative Study of Machine Learning Models
Author(s): Md. Arshad, Pragga Dutta, Sanjida Jahan, Ovi Shukla Das and Abhijit Pathak*
Customers need to be able to talk to businesses in more than one language in the very competitive world of e-commerce. Sentiment analysis is now a vital tool for improving business efficiency and making smart choices. Previous studies on sentiment analysis focused on English, which made it harder to get accurate results from reviews written in English. To make the model more accurate, English reviews were added to a machinelearning model. In a thorough study, accuracy, precision, recall, and F1 scores were used to compare three algorithms. The dataset, which was discovered on Kaggle, was meticulously labeled with positive, negative, and neutral sentiments. After preprocessing the data, machine learning approaches were employed to train the model and evaluate its performance. The accuracy of Multinomial Naive Bayes (MNB) and Random Forest (RF) was 93%, while Decision Tree (DT) was 91%... View More»
DOI:
10.14303/2315-5663.2023.115