基于卷积神经网络的短文本情感分类

Short Text Sentiment Classification Based on Convolutional Neural Network

近年来,卷积神经网络模型常常被用于文本情感分类的研究中,但多数研究都会忽略文本特征词本身所携带的情感信息和中文文本分词时被错分的情况.针对此问题,提出一种融合情感特征的双通道卷积神经网络情感分类模型(Dual-channel Convolutional Neural Network sentiment classification model fused with Sentiment Feature,SFDCNN).该模型在构造输入时以一条通道构造融合情感特征的语义向量矩阵以获取到更多的情感类型信息,以另一条通道构造文本字向量矩阵以降低分词错误的影响.实验结果表明,SFD-CNN模型准确率高达92.94%,要优于未改进的模型.

In recent years,the convolutional neural network model is often used in the research of text emotion classification.However,most of researches ignore the emotional information carried by the text feature words themselves and the wrong segmentation of Chinese text.Aiming at this problem,a Dual-channel Convolutional Neural Network sentiment classification model fused with Sentiment Feature(SFD-CNN)is proposed.In the model,one channel is used to construct the semantic vector matrix of emotional features to get more emotional type information,and another channel is used to construct the text word vector matrix to reduce the impact of segmentation errors.The experimental results show that the accuracy of SFD-CNN model is as high as 92.94%,which is better than that of the unmodified model.