Predicting the helpfulness of online reviews using multilayer perceptron neural networks |
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| Affiliation: | 1. College of Business Administration, Sejong University, Seoul 143-747, Republic of Korea;2. Department of Digital Contents, Sejong University, Seoul 143-747, Republic of Korea;1. Biomedical Knowledge Engineering Laboratory, Seoul National University, Republic of Korea;2. Dental Research Institute, Seoul National University, Republic of Korea;3. Institute of Human-Environment Interface Biology, Seoul National University, Republic of Korea;1. College of Computer Science and Technology, Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education, Jilin University, 130012 Changchun, China;2. College of Computer Science and Engineering, Changchun University of Technology, 130012 Changchun, China;1. University of Information Technology, Ho Chi Minh City, Vietnam;2. Department of Computer Science and Computer Engineering, La Trobe University, VIC 3086, Australia;1. Department of Information Management, National Chung Cheng University, Chiayi, 62102, Taiwan, ROC;2. Department of Business Information Systems, Western Michigan University, 3344 Schneider Hall, Kalamazoo, MI 49008-5412, United States;3. Department of Information Management, National Chung Cheng University, Chiayi, 62102, Taiwan, ROC |
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| Abstract: | With the great development of e-commerce, users can create and publish a wealth of product information through electronic communities. It is difficult, however, for manufacturers to discover the best reviews and to determine the true underlying quality of a product due to the sheer volume of reviews available for a single product. The goal of this paper is to develop models for predicting the helpfulness of reviews, providing a tool that finds the most helpful reviews of a given product. This study intends to propose HPNN (a helpfulness prediction model using a neural network), which uses a back-propagation multilayer perceptron neural network (BPN) model to predict the level of review helpfulness using the determinants of product data, the review characteristics, and the textual characteristics of reviews. The prediction accuracy of HPNN was better than that of a linear regression analysis in terms of the mean-squared error. HPNN can suggest better determinants which have a greater effect on the degree of helpfulness. The results of this study will identify helpful online reviews and will effectively assist in the design of review sites. |
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| Keywords: | Neural networks" },{" #name" :" keyword" ," $" :{" id" :" k0010" }," $$" :[{" #name" :" text" ," _" :" Helpfulness Prediction model Determinants of helpfulness |
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