Integrating artificial neural networks with rule-based expert systems
Decision Support Systems - Special issue on neural networks for decision support
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry
Journal of Management Information Systems
Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications
IEEE Intelligent Systems
International Journal of Electronic Commerce
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
IEEE Transactions on Knowledge and Data Engineering
Identifying helpful reviews based on customer's mentions about experiences
Expert Systems with Applications: An International Journal
Discovering business intelligence from online product reviews: A rule-induction framework
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
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.