Sentiment Analysis on Reviews of E-commerce Sites Using Machine Learning Algorithms

Abstract

Customers of e-commerce platforms exchange their thoughts with such kinds of languages. In the age of the present competitive business world, sentiment analysis is widely used in the e-commerce industry to improve efficiency and better understand to make business decisions. Earlier research on sentiment analysis was in English but there is no such significant work in Bangla language and Romanized Bangla language reviews. Therefore, we have developed a machine learning model where reviews on three different languages (Bangla, English, and Romanized Bangla) are used and applied six machine learning algorithms. We have demonstrated a comparative analysis with existing work and have discussed the detailed accuracy, precision, recall, F1 scores, and ROC area. We have prepared three datasets and labeled all the reviews data as Negative, Positive, Neutral, Slightly Negative, and Slightly Positive sentiment. To perform the analysis, the preprocessed datasets were trained using machine learning techniques, and the model performances is evaluated. For the Bangla dataset, Support Vector Machine(SVM) algorithm performed best by achieving 94% accuracy and for the English and Romanized Bangla dataset, Random Forest algorithm performed best by achieving 93% and 94% accuracy respectively.