Paper Info
Title
Predicting the helpfulness score of online reviews using convolutional neural network
Abstract
The smart cities aim to provide an infrastructure to their citizens that reduces both their time and effort. An example of such an available infrastructure is electronic shopping. Electronic shopping has become the hotbeds of many customers as it is easier to judge the quality of the product based on the review information. The purpose of this study is to predict the best helpful online product review, out of the several thousand reviews available for the product using review representation learning. The prediction is done using a two-layered convolutional neural network model. The review texts are embedded into low-dimensional vectors using a pre-trained model. To learn the best features of the review text, three filters are used to learn tri-gram, four-gram, and five-gram features of the text. The proposed approach is found to be better than existing machine learning based models which used hand-crafted features. The very low value of mean squared error confirms the prediction accuracy of the proposed method. The proposed method can be easily applied to any kind of review as the features are calculated only from the review text and not from other domain knowledge. The proposed model helps in predicting the helpfulness score of new reviews as soon as it gets posted on the product review page.
Year
DOI
Venue
2020
10.1007/s00500-019-03851-5
Soft Computing
Keywords
DocType
Volume
E-commerce, Convolutional neural network, Machine learning, Helpfulness, Online generated reviews, Deep learning
Journal
24
Issue
ISSN
Citations 
15
1433-7479
2
PageRank 
References 
Authors
0.38
47
3
Name
Order
Citations
PageRank
Sunil Saumya120.38
jyoti singh2748.02
Yogesh Kumar Dwivedi3155897.69