Research On Product Ranking Based On Online Reviews Mining

The development of Web 2.0 era has added a spur to consumers' online information sharing behaviors.The forms of information are increasingly diversified,and information carriers have also expanded from online shopping platforms to forums in various professional fields.Faced with massive and rapidly increasing online reviews,it becomes an important issue to implement these applications through efficient data mining methods.Current methods of research data selection mostly focus on the online reviews of a specific product,which are not suitable for data mining of hybrid product reviews in the forums.secondly,without further research of user recommendation,present studies of online reviews are generally unitary sentiment discovery and are lack of realistic significance.In this study,the deficiency of current research is firstly found out through literature review.Therefore,in this paper,conditional random field algorithm is used to carry out the entity extraction of hybrid product reviews.Analyze the sentiments of those reviews and build information fusion networks to obtain product ranking result.In the process of entity extraction research,conventional word features and similarity features are combined to get more accurate results.The similarity can obtain from deep learning-based word embedding.On the other hand,atomic template and combined template are selected to merge.Experiments are carried out from two dimensions of feature and feature templates.The combination results of higher accuracy of entity extraction are obtained.Obtain specific entity reviews by entity recognition.Dictionary-based calculations of sentiment intensity for certain product are carried out.The weightings of each product features are determined through the LDA topic model.Finally,a directed graphical model is built and an improved PageRank algorithm was adopted to calculate the importance of each network node.The objective sentiment values based on massive online reviews and the subjective sentiment values based on user preferences are comprehensively analyzed.The final ranking results of each product are determined.The results have demonstrated that the data mining method proposed in this paper is viable.The proposed method not only t makes full use of vast online review resources,but also takes into account the personalized preference of consumers in the modeling,which makes the experimental results more realistic.It provides new research ideas for online review mining and recommendation.