Research And Application Of Mining Algorithm For Online Chinese Comments

The development of the online shopping industry is becoming more and more mature,and more and more consumers are making comments on the shopping site.Product reviews reflect the consumers' attitudes and views on the products.They have a very practical value.On the one hand,product reviews can affect other consumers' buying intentions;On the other hand,product reviews feedback information to businesses to facilitate improvements in products and services.However,it is very difficult to get meaningful information from a large number of texts data of product reviews in a short time.It is very important to study the extraction methods of Chinese comments mining to improve the efficiency of texts information extraction.The main contents are as follows:To take the Chinese online commentary texts as the main research object.Firstly,the subjective comment texts are separated from the product reviews by artificial way.And then use the natural language processing technology to deal with Chinese online comments.In this paper,an improved Chinese online comment mining algorithm is proposed to solve the problem of low recall and low precision in Chinese online product reviews.The method divides the comment text into four kinds of sentence structure according to the expression of natural language.And then extract the product opinion words and characteristic words in the various comment texts based on the adverbs.The experimental results show that this method can effectively improve the recall rate and Precision rate of Chinese product reviews.Finally,based on "extended version of word forest synonyms" to merge synonyms for the characteristics of the product review texts.And get the final characteristic words and opinions words.Experiments show that this method improves the validity of synonym merges.In this paper,the Chinese product comments mining method based on the adverbs,can make full use of natural language expression characteristics,and automatically extract the characteristics and opinions of Chinese product reviews...