融合多特征的分段卷积神经网络对象级情感分类方法

对象级情感分类旨在判断句子中特定对象的情感极性类别。在现有基于卷积神经网络的研究中,常在模型的池化层采用最大池化操作提取文本特征作为句子表示,该操作未考虑由对象所划分的上下文,因此无法得到更细粒度的对象上下文特征。针对该问题,该文提出一种融合多特征的分段卷积神经网络(multi-feature piecewise convolution neural network,MP-CNN)模型,根据对象将句子划分为两个部分作为上下文,并在池化层采用分段最大池化操作提取上下文特征。此外,该模型还将有助于情感分类的多个辅助特征融入其中,如词的相对位置、词性以及词在情感词典中的情感得分,并通过卷积操作计算词的注意力得分,有效判断对象的情感极性类别。最后在SemEval 2014数据集和Twitter数据集的实验中,取得了较基于传统机器学习、基于循环神经网络以及基于单一最大池化的卷积神经网络分类模型更好的分类效果。

Multi-Feature Piecewise Convolution Neural Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification aims at judging the sentiment polarity of a particular aspect in a sentence. In the existing research on convolution-based neural networks, the maximum pooling operation is often used to extract text features as sentence representation in the pooling layer of the model. This operation does not consider the context divided by the aspect and fails to get finer-grained aspect context features. To solve this problem, this paper proposes a multi-feature piecewise convolution neural network (MP-CNN) model. According to the aspect, the sentence is divided into two parts of context, and in the pooling layer, the maximum pooling operation is used to extract the context features. In addition, this paper also integrates several auxiliary features into the model, such as relative position of words, part of speech and sentiment score of words in sentiment lexicon, and calculates the attention score of words through convolution operation. The experiments of SemEval 2014 and Twitter datasets confirm the best performance among the baselines.