基于多注意力网络的特定目标情感分析

Target-Specific Sentiment Analysis Based on Multi-Attention Network

作为自然语言处理领域的经典研究方向之一,特定目标情感分析的任务是根据句子上下文语境判别特定目标的情感极性,而提升该任务表现的重点在于如何更好地挖掘特定目标和句子上下文的语义表示.本文提出融合短语特征的多注意力网络(Phrase-Enabled Multi-Attention Network,PEMAN),通过引入短语级别语义特征,构建多粒度特征融合的多注意力网络,有效提高模型的表达能力.在SemEval2014 Task4 Laptop、Restaurant数据集上的实验结果表明,与基准模型相比,本文提出的PEMAN模型在准确率上有一定提升.

As one of the classic research directions in the field of natural language processing,the task of target-specific sentiment analysis is to determine the sentiment polarity of a specific target based on contexts.The key to improve the performance of this task is how to better mine the semantic representation of specific target and contexts.This study proposes a multi-attention network with phrase features.By introducing phrase-level semantic features,a multi-attention network with multi-granularity features is constructed to improve the expression ability of the model effectively.The experimental results on the SemEval2014 Task4 Laptop and Restaurant datasets show that the PEMAN model proposed in this study has a certain improvement in accuracy compared with the benchmark model.