一种面向目标的情感极性分析方法

A Method for Targeted Sentiment Analysis

面向目标的情感分析是细粒度情感分析的重要任务之一,旨在预测句子中给定目标实体的情感极性.当前大多数研究方法忽略了句法结构信息,在情感判别时往往会关注无关词汇,从而使分类性能下降.为此,设计了一种新的引入句法结构的模型,该模型利用双向预训练编码器和作用于依存句法树的图卷积网络分别捕获文本的上下文信息和句法结构信息,并使用多头注意力机制进行信息聚合得到目标的情感分类表征.此外,还将该模型与现有的领域自适应方法相结合,同时向模型中引入领域知识和句法结构知识,进一步提升了模型效果.在几个常用的标准数据集上的实验结果表明了上述模型的有效性。

Targeted sentiment analysis(TSA)is a crucial task for fine-grained public opinion mining,which focuses on predicting the sentiment polarity towards a specific target in a given sentence.Most of existing works ignore the syntactic structure of the context sentence,and may pay attention to irrelevant context words when making sentiment judgments.To tackle the problem,a novel syntax aware model is proposed for TSA,which integrates the pre-trained bidirectional encoder representation from transformers models and a graph convolutional network over the dependency tree of the sentence to capture the context information and syntactic structure information of the sentence respectively.The proposed model uses the multi-head attention mechanism to aggregate the information to obtain the final target sentiment representation.The proposed model is also combined with the existing domain adaptive method to introduce domain knowledge and syntactic knowledge,which further improves the performance.The experimental results on several widely-used benchmark datasets demonstrate the effectiveness of the proposed model.