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- W4313342034 abstract "Online shopping has drastically reduced the tiresome job of reaching out to offline stores and selecting goods in a limited product range. Almost everything is available online, right from the basic essential goods to costlier electrical appliances in today's world. The sellers increasingly misuse these massive online platforms for increasing their product sales by posting false reviews. Consumer engagement reports suggest that around 82% of customers read online reviews before purchasing a product online. So these reviews are crucial for them to decide if the product suits them and is reliable. So, in this paper, we propose various machine learning models for detecting fake reviews and delineate and do a comparative analysis of each model to determine the best algorithm. This work plays a vital role in reducing and checking fake reviews." @default.
- W4313342034 created "2023-01-06" @default.
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- W4313342034 date "2022-01-01" @default.
- W4313342034 modified "2023-10-11" @default.
- W4313342034 title "Online Shopping Fake Reviews Detection Using Machine Learning" @default.
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- W4313342034 doi "https://doi.org/10.1007/978-3-031-22405-8_24" @default.
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