Abstract:
E-commerce stands for electronic commerce as it refers to conducting purchases and sales through the internet. The worldwide marketplace received its transformation through ecommerce because it lets companies and customers conduct transactions across limits while providing quick and easy connection. Electronic commerce continues to grow exponentially because of technological progress combined with greater use of the internet and smartphones by everyone. Various market types participate within this field including B2B (business-to-business), B2C (business-to-consumer), C2C (consumer-to-consumer) and C2B (consumer-to-business) which strengthen both the economy and modify consumer conduct. E-commerce has taken off overwhelmingly in Bangladesh throughout the past decade because of upgraded internet connectivity and advancing online payment capabilities. The E-commerce platforms Daraz and Chaldal and AjkerDeal have become widely popular since users choose these platforms for various shopping needs including fashion items electronics and grocery purchases. The e-commerce sector of Bangladesh delivers substantial impact through business expansion and reduced operational costs as well as offering convenient home delivery options to customers. The digital economy has established employment sectors in logistical services and customer service and digital marketing respectively which together support national economic expansion. This research project demonstrates that deep learning models achieve outstanding capability in determining customer satisfaction levels on Bangladeshi eCommerce platforms. The Bi-LSTM, LSTM, CNN and RNN models underwent comparison through accuracy evaluation for their ability to categorize customer sentiment into Highly Satisfied and Satisfied and Non-Satisfied groups. The evaluation revealed CNN's superiority by delivering 99.20% accuracy while RNN achieved 93.71% accuracy then Bi-LSTM reached 90.17% and LSTM stopped at 77.60%. The research results demonstrate that CNN provides optimal performance for sentiment evaluation needs in eCommerce which delivers actionable client preferences to businesses to improve both experience and satisfaction levels for customers.
