A Practicable E-commerce-Based TextClassification System

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This article examines the features of the dealer's brush list evaluation material in light of research findings on misleading assessment and identification of online purchasing. A Gated Recurrent Unit (GRU) model using keyword weighting is presented as a solution to the issue that it is challenging for the DL model to collect the feature data of the whole assessment text in a false evaluation identifying job. The TFIDF technique is first used to generate the list of keywords, and then that list's weight is applied to the word vector. Finally, a weighted vector of words is categorised using this method of the model to finish the recognition job of erroneous evaluation, replacing the pooling component of the GRU model with a constrained Boltzmann machine. By using a variety of text categorization algorithms and comparing their results in terms of correctness and performance, this research aspires to represent the practical benefits of applications that use machine learning in the real world. We built a system that can run several text classification algorithms, and we used that system to create models that were educated using actual data taken from E-Commerce, a virtual fashion e-commerce platform. The Convolutional Neural Network technique achieved the greatest mean accuracy of 96.08% (with a range of 85.44% to 99.99%) with an average deviation of 5.65%.