Emotional element detection and tendency judgment based on mixed model with deep features

Abstract

With the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. Product reviews contain subjective feelings of customers who have used some products, more and more customers browse a large number of online reviews in order to know other customers word-of-mouth of product and service to make an informed choice. Manufacturers also need accurate user feedback from product reviews to improve their goods. However, a large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields (CRFs) to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine (SVM) to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network (NN) to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.