基于多通道特征和自注意力的情感分类方法

针对情感分析任务中没有充分利用现有的语言知识和情感资源，以及在序列模型中存在的问题：模型会将输入文本序列解码为某一个特定的长度向量，如果向量的长度设定过短，会造成输入文本信息丢失.提出了一种基于多通道特征和自注意力的双向LSTM情感分类方法（MFSA-BiLSTM），该模型对情感分析任务中现有的语言知识和情感资源进行建模，形成不同的特征通道，并使用自注意力重点关注加强这些情感信息.MFSA-BiLSTM可以充分挖掘句子中的情感目标词和情感极性词之间的关系，且不依赖人工整理的情感词典.另外，在MFSA-BiLSTM模型的基础上，针对文档级文本分类任务提出了MFSA-BiLSTM-D模型.该模型先训练得到文档的所有的句子表达，再得到整个文档表示.最后，对5个基线数据集进行了实验验证.结果表明：在大多数情况下，MFSA-BiLSTM和MFSA-BiLSTM-D这两个模型在分类精度上优于其他先进的文本分类方法.

Sentiment Classification Method Based on Multi-channel Features and Self-attention

The purpose of this study is for the problem that the existing language knowledge and emotion resources are not fully utilized in the emotion analysis tasks, as well as the problems in the sequence model:the model will decode the input text sequence into a specific length vector, if the length of the vector is set too short, the information of input text will be lost. A bidirectional LSTM sentiment classification method is proposed based on multi-channel features and self-attention (MFSA-BiLSTM). This method models the existing linguistic knowledge and sentiment resources in sentiment analysis tasks to form different feature channels, and uses self-attention mechanism to focus on sentiment information. MFSA-BiLSTM model can fully explore the relationship between sentiment target words and sentiment polar words in a sentence, and does not rely on a manually compiled sentiment lexicon. In addition, this study proposes the MFSA- BiLSTM-D model based on the MFSA-BiLSTM model for document-level text classification tasks. The model first obtains all sentence expressions of the document through training, and then gets the entire document representation. Finally, experimental verifications are conducted on five sentiment classification datasets. The results show that MFSA-BiLSTM and MFSA-BiLSTM-D are superior to other state-of-the-art text classification methods in terms of classification accuracy in most cases.