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Prediction of customer review’s helpfulness based on sentences encoding using CNN-BiGRU model

Surya Prakash Sharma, Laxman Singh, Rajdev Tiwari

Abstract


The infrastructure of smart cities is intended to save citizens’ time and effort. After COVID-19, one of such available infrastructure is electronic shopping. Online consumer reviews have a big influence on the electronic retail market. A lot of customers save time by deciding which products to buy online by evaluating the products’ quality based on user reviews. The goal of this study is to forecast if reviews based on reviews representation mining will be helpful while making online purchases. Predicting helpfulness is used in this suggested study to determine the usefulness of a review in relation to glove vector encoding of reviews text. Using an encoding-based convolution neural network and a bidirectional gated recurrent unit, the authors of this study constructed a classification model. The suggested model outperformed these baseline models and other state-of-the-art techniques in terms of classification outcomes, reaching the greatest accuracy of 95.81%. We also assessed the effectiveness of our models using the criteria of accuracy, precision, and recall. The outcomes presented in this study indicate how the proposed model has a significant influence on enhancing the producers’ or service providers’ businesses.


Keywords


convolution neural network (CNN); customer reviews helpfulness; BiGRU; machine learning (ML); binary classification; natural language processing

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References


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DOI: https://doi.org/10.32629/jai.v6i3.699

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Copyright (c) 2023 Surya Prakash Sharma, Laxman Singh, Rajdev Tiwari

License URL: https://creativecommons.org/licenses/by-nc/4.0