Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (10): 69-74.doi: 10.11896/jsjkx.190700034

• Database & Big Data & Data Science • Previous Articles     Next Articles

Helpfulness Degree Prediction Model of Online Reviews Fusing Information Gain and Gradient Decline Algorithms

FENG Jin-zhan, CAI Shu-qin   

  1. School of Management,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2019-07-03 Revised:2019-09-27 Online:2020-10-15 Published:2020-10-16
  • About author:FENG Jin-zhan,born in 1981,postgra-duate.His main research interests include business intelligence,management information and network complaint handling.
    CAI Shu-qin,born in 1955,Ph.D,professor,Ph.D supervisor.Her main research interests include business intelligence and management information system.
  • Supported by:
    National Natural Science Foundation of China (71371081) and Specialized Research Fund for the Doctoral Program of Higher Education (20130142110044)

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Abstract: Because it is impossible to predict whether the text content of online product reviews is helpful for viewers,many reviewers write a large number of unhelpful reviews,which increases the cost of information search for potential consumers,and even reduces the possibility of potential consumers buying products.In order to improve the helpful online reviews rate of e-commerce platform and provide test function for reviewers,a prediction model of online reviews helpfulness is established.According to the text characteristics of online reviews,the model chooses three features of online reviews:the number of words,the helpful value of words,and the number of product features,to construct a model for predicting the helpfulness of online reviews.The helpful value is the information gain of words to distinguish the helpfulness of online reviews.And then,according to a large number of online reviews,by using the gradient descent algorithm,the model parameters are solved.The experimental results show that with the increase of the number of words,helpful value of words and the number of product features,the helpfulness of reviews increases continuously.The online reviews are divided into three levels:general,helpful and very helpful.The general predicted accuracy of online reviews is 92.96%,helpful accuracy is 94.83%,and very helpful accuracy is 67.63%.The average accuracy,recall and F1 of the model are 85.05%,82.81% and 83.72%,respectively.The results verify the feasibility of the model to predict the helpfulness of online reviews.

Key words: Gradient descent algorithm, Helpfulness degree, Information gain, Online reviews

CLC Number: 

  • TP391
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