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Bangla Fake Food Review Detection Using Machine Learning Algorithms

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dc.contributor.advisor Dr. Mohammad Arifuzzaman
dc.contributor.author Uddin, Md Ashraf
dc.contributor.author Paul, Tusher
dc.contributor.author Ahmmed, Md Riaz
dc.date.accessioned 2023-01-30T10:41:50Z
dc.date.available 2023-01-30T10:41:50Z
dc.date.issued 2023-01-18
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3868
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract In recent years, Fake Review Detection has emerged as a key and well-liked topic for both business and research due to the explosive growth of e-commerce platforms. In today's e-commerce, internet reviews are crucial for helping consumers make the best decisions. Numerous social media platforms and marketing websites host millions of reviews of various goods and services. Customers post reviews of products based on their personal experiences, and other buyers can learn more from these reviews before making a purchase choice. However, since there are no restrictions on what can be posted in a review on any internet platform, it can often be quite difficult to identify the real reviews. According to them, anyone may submit reviews, which increases the quantity of bogus reviews and might provide false information, misleading a buyer. These reviews, whether they are phony or real, have a big impact on an organization's revenue and reputation. Positive reviews typically increase sales and profit while negative reviews negatively impact a company's reputation. We are motivated by this situation to discern between bogus and real reviews. In this study, we cover several machine learning techniques (Naive Bayes, Random Forrest, K-Nearest Neighbor, Logistic Regression, Support Vector Machine (Linear), Decision Tree), and we try to determine whether the review is credible or not using those algorithms. We used a confusion matrix and examined the outcomes of the experiment. We also talk about some of the difficulties we had writing this thesis and some of our future plans for it. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00260
dc.subject Fake Review Detection, Detection Using Machine Algorithms, E-commerce platforms en_US
dc.title Bangla Fake Food Review Detection Using Machine Learning Algorithms en_US
dc.type Thesis en_US


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