Aspect Extraction of Case Microblog Based on Double Embedded Convolutional Neural Network

Aspect extraction of the microblog involved in the case is a task in a specific domain.The expression of aspect words is diverse and the meaning is different from that of the general domain.Only relying on the word embedding in the general domain,these aspect words cannot be well represented.This paper proposes a method for extracting aspect words from microblogs by using both domain word embedding and generic word embedding.Firstly,all the microblogs involved in the case is pre-trained to obtain the embedding layer with the characteristics of the involved domain.Secondly,the microblog comments are input into two embedding layers to obtain the characterization results of the aspect words in different domains,and perform the splicing operation.Then,the features related to the case are extracted through the convolution layer.Finally,the classifier is used to label the sequence to extract aspect words involved in the case.The experimental results show that the F1 value of the proposed method reaches 72.36% and 71.02% respectively on the data sets of #Chongqing bus falling into the river# and #Mercedes Benz female driver rights protection#,which is better than the existing benchmark models,and verifies the influence of word embedding in different domains on the aspect extraction of the microblogs.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.