Abstract: Usually,the rating of the product on the website is obtained by averaging the rating of the product review,but this method relies heavily on the rating of reviews,which is not accurate enough for products with fewer reviews.Different from the traditional product scoring mechanism,this paper proposed a hierarchical neural network model for the overall scoring of products based on the text information of them,which can analyze the fair scores of products from limited reviews.In the product review,there is a hierarchical structure of [word-sentence-review-product],so the structure of three-layer GRU is used to get the representations of the sentences,reviews and products separately,so as to predict the final score of the product.In addition,this paper also makes additional output to the review layer to further improve the accuracy of the prediction.Experiments on the two prediction tasks of regression and classification show that the hierarchical structure of the model plays a crucial role in predicting the score of the product,and the score of outputting comment can further improve the prediction accuracy.

Key words: Hierarchical neural network, Product rating, Rating prediction