Paper Contents
Abstract
The Amazon and Flipkart datasets have been used in a number of tests. It is applied to a number of products on two e-commerce websites named Products of all kinds are described on Amazon and Flipkart. The. The PCSA system gathers review data for 10 categories, including mobile devices such as laptops, phones, cameras, air conditioners (AC), routers, TVs, books, and articles comprising apparel, kitchenware, furnishings, and transportable goods. We've tested this system on 37,344 evaluations.Numerous observations were made based on the experimental findings. Both positive and negative ratings were most prevalent in the "Mobile Phone" category on Flipkart and Amazon. Senti WordNet and the logistic regression classifiers produced the best ratings of 4.24 and 4.51 for products sold on Amazon and Flipkart, respectively. Product star ratings on Flipkart were anticipated by the PCSA method to be greater than those on Amazon. When compared to Amazon, Flipkart received greater ratings for all classification algorithms. First, the login and registration information is set. The user is required to supply the set of links, the number of links, and the categorization technique name. The training phase and the testing phase are the two stages of this system. The training step uses input from known comments from sources such as Flipkart and Amazon. After that, they undergo preprocessing and are organized. These comments provide the segmented sentences and elements that are taken from them. These functions are used to train all five classifiers. After that, the system asks the user to choose the classifier, which separates the comments into groups that are mutually exclusive.
Copyright
Copyright © 2023 PAWAR SANTOSH SANJAY. This is an open access article distributed under the Creative Commons Attribution License.
