ABSTRACT
Online marketing and e-commerce companies are booming in Bangladesh in this age of internet technology. As more people were afflicted with the COVID-19 epidemic, internet purchasing became the primary channel for closure shopping and was considered the safest method. The enterprises were pushed to appear online. There are many online service providers, such beneficial for individuals, but it also calls into question the quality of the products with services. Therefore, it is simple for new clients to be deceived, when doing internet purchasing. The enormous volume of tech gadget review data that is generated online every day can be examined for the purpose of assessing public sentiment and assisting in market intelligence. While the study of sentiment classification has advanced greatly in languages with abundant resources, it is still in the preliminary stage for languages with limited resources, such as Bengali. This work proposes a model for classifying the sentiment on online Bengali tech gadget reviews into three basic categories- positive, negative, and neutral. For this purpose, around 6015 Bengali tech review data is collected. Various Machine Learning techniques are then applied along with different feature extraction techniques. After evaluating the performance, the Random Forest outperforms the rest of other techniques, having a maximum accuracy of 86.28%. © 2022 IEEE.