基于领域特有情感词注意力模型的跨领域属性情感分析

Domain Specific Sentiment Words Based Attention Model for Cross-Domain Attribute-Oriented Sentiment Analysis

虽然近年来情感分析相关研究取得很大进展,但跨领域属性情感分析仍是一个挑战。现有的方法主要关注源领域和目标领域的共有信息,忽略了目标领域的特有信息。此外,情感词作为句子中的重要信息,不仅能反映属性的情感极性,而且可以被划分为共有情感词和特有情感词。针对目标领域的特有信息和情感词,该文提出领域特有情感词注意力模型(DSSW-ATT)。该模型设立两个独立的子空间,分别使用注意力机制提取共有情感词特征和特有情感词特征,并建立相应的共有特征分类器和特有特征分类器,同时使用协同训练方法融合这两种特征。该文还构建了酒店领域(源领域)和手机领域(目标领域)的属性级用户评论数据集。在该数据集上的实验结果表明,该方法明显优于基线方法。

Cross-domain attribute-oriented sentiment analysis is a challenging issue.To explore domain-specific features for cross-domain attribute-oriented sentiment analysis,we divide sentiment words into shared sentiment words and specific sentiment words,and thus propose a domain specific sentiment words attention model(DSSW-ATT).Firstly,we set up two independent subspaces and use attention mechanism to extract shared sentiment word features and specific sentiment word features,respectively.Then,we establish a shared feature classifier and a specific feature classifier.Finally,we use co-training to combine the two kinds of information.To examine our method,we build a couple of attribute-level online review datasets in the hotel domain(as the source domain)and the phone domain(as the target domain).Experimental results show that the proposed method outperforms the baselines.