Implicit Attribute Recognition of Online Clothing Reviews Based on Bidirectional Gated Recurrent Unit-Conditional Random Fields

Sentiment analysis has been widely used to mine users'opinions on products,product attributes and merchants'response attitudes from online product reviews.One of the key challenges is that the opinion words in some reviews lack obvious evaluation objects(product attributes).This paper aims to identify implicit attributes from online clothing reviews,and presents a unified model which applies a unified tagging scheme.Our model integrates the indicator consistency(IC)module on the basis of bidirectional gated recurrent unit(BiGRU)with a conditional random fields(CRF)layer(BiGRU-CRF),which denoted as BiGRU-IC-CRF.On the 9640 comments data set of a certain clothing brand,the comparative experiment is carried out by BiGRU,BiGRU with an IC layer(BiGRU-IC)and BiGRU-CRF.The results show that this method has a higher recognition rate,and the F1 value reaches 85.48%.The method proposed in this paper is based on character labeling,which effectively avoids the inaccuracy of word segmentation in natural language processing.The IC module proposed in this paper can maintain the consistency of the product attributes corresponding to the opinion words,thereby enhancing the recognition ability of the original BiGRU-CRF method.This method is not only applicable to the implicit attributes recognition in clothing reviews,but also helpful to other fields implicit attribute recognition of product reviews.