Ashraf, Faisal BinHawladar, MohibullahGhosh, ArjanRaad, Zaoyad KhanChowdhury, Wali AhadShehan, Md Sazzad Hossain2021-09-042021-09-0420212021ID 17101058ID 20141045ID 17101077ID 17301057ID 17301150http://hdl.handle.net/10361/14970Cataloged from PDF version of thesis.Includes bibliographical references (pages 41-42).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.E-commerce is gaining traction in today’s digitalized environment by taking products closer to customers without forcing them to leave their homes. A customer must study hundreds of reviews before making a purchase. The amount of internet evaluations for a single product can easily approach millions and make tracking and understanding of client feedback difficult. In the era of machine learning, however, it would be much easier to gain thousands of input and knowledge from them if a model were employed to polarize and understand from them. Consequently, sentiment analysis is a new study area combining natural language processing and text analytic to extract subjective information from sources and classify the polarity of expressed sentiments. We have employed Vector Machine Support, Naive Bays, Decision Tree, Random Forest, Logistic Regression, and MLP Classifiers for large-scale supervised education on the Amazon dataset and obtained satisfactory results. In the meantime, the MLP classifier produced the best results. Finally, this paper discusses sentiment analysis and product feedback opinion mining.42 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Sentiment AnalysisOpinion MiningAmazon Review AnalysisSupport Vector Machine (SVM)Decision treeLogistic RegressionMLPRandom Forest AnalysisSentiment analysis.Thesis