| Along with the appearance of online reviews, comes numerous product reviews including consumer experience and review information which is of great values for enterprises. The successful use of online reviews provides consumers with an immediate access to share their opinions involving products and services. Through the analysis and researches of online reviews, consumers are able to formulate their shopping plan by the comparison of different products and manufacturers can learn about the advantages and weaknesses of their products and services in order to grasp the needs of consumers and make their products and services better. Traditional classification method can effectively extract the product opinions, but not for Chinese reviews. In order to improve the effectiveness of product reviews classification, firstly we present a kind of feature extraction method based on the length of feature to improve classification accuracy; secondly we design a comment sample automatic annotation methods and construct the classification model; and finally, take 1710 product reviews from Jingdong Mall and proposed this methods. The results show that this method could improve the classification accuracy significantly. |
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