基于神经网络融合标签相关性的多标签情感预测研究

近年来,多标签分类任务(MLC)受到了广泛关注。传统的情感预测被视为一种单标签的监督学习,而忽视了多种情感可能在同一实例中共存的问题。以往的多标签情感预测方法没有同时提取文本的局部特征和全局语义信息,或未考虑标签之间的相关性。基于此,该文提出了一种基于神经网络融合标签相关性的多标签情感预测模型(Label-CNNLSTMAttention,L-CLA),利用Word2Vec方法训练词向量,将CNN和LSTM相结合,通过CNN层挖掘文本更深层次的词语特征,通过LSTM层学习词语之间的长期依赖关系,利用Attention机制为情意词特征分配更高的权重。同时,用标签相关矩阵将标签特征向量补全后与文本特征共同作为分类器的输入,考察了标签之间的相关性。实验结果表明,L-CLA模型在重新标注后的NLP&CC2013数据集上拥有较好的分类效果。

Neural Network Based Multi-label Sentiment Analysis via Tag Fusion

In recent years, muliti-label-classification-task(MLC) has been widely concerned. Traditional sentiment analysis is regarded as a single label supervised learning, while ignoring the problem that multiple sentiments may coexist in the same instance. This paper proposes a multi-label sentiment analysis model (Label-CNN_LSTM_Attention,L-CLA) to fuse the labels via neural network. With the Word2Vec as the input, CNN and LSTM are combined, with the CNN layer dealing with deep word features in the text and the LSTM layer capturing the long-term dependence between words. The attention mechanism is adopted to assign higher weight to the affective words, and the label correlation matrix is integrated to pad the label feature vector as part of the input. The experimental results show that the L-CLA model has a good classification effect on the retagged NLP & CC2013 data set.