Abstract:
In the fast-changing landscape of e-commerce, establishing trust has become essential for sustainable development, particularly in regions like Bangladesh where customer confidence is poor. This study analyzes customer reviews in the Bangladeshi e-commerce industry using few of the Machine Learning approaches, especially utilizing Natural Language Processing (NLP). The primary focus is on identifying patterns and sentiments in Bengali reviews in order to educate businesses about consumer preferences as well as enhance services, ultimately building trust. This research work evaluates effectiveness of very well-known Machine Learning technique call natural language processing for processing a huge number of Bengali e-commerce reviews while considering specific linguistic and contextual differences. The study also intends to derive useful insights from consumer feedback analysis in order to improve products and services, enhance customer satisfaction, and achieve a competitive edge in the Bangladeshi e-commerce environment. Additionally, the analysis of sentiment findings is expected to be beneficial to the growth and development of Bangladesh's e-commerce sector, with an eye on possible challenges with implementing data-driven initiatives that depend on consumer feedback. The research methodology is based on collecting data from Kaggle and efficiently pre-processing text using Natural Language Processing. To gain more precise outcomes of my models, unigram, bi-gram, and trigram features will be integrated into the Linear Support Vector Machine, Karnel Support Vector Machine, Random Forest Classifier, Decision Tree Classifier, Naive Bayes Classifier and Logistic Regression techniques. The research study has achieved 90.76% accuracy using the Random Forest algorithm utilizing Bigram feature.
