Words alignment based on association rules for cross-domain sentiment classification
Words alignment based on association rules for cross-domain sentiment classification
复制标题
基于关联规则的词对齐跨域情感分类
DOI：
10.1631/fitee.1601679
复制
发表时间：
2018-04-01
影响因子：
3
通讯作者：
Bhanu, Bir
中科院分区：
文献类型：
--
作者：
Jia, Xi-bin;Jin, Ya;Bhanu, Bir
情感数据的自动分类（例如，评论，博客）在企业用户管理系统中有很多应用，可以帮助我们了解人们对产品或服务的态度。然而，很难为不同的领域训练准确的情感分类器。其中一个主要原因是人们在不同的领域中往往使用不同的词语来表达同一种情感，我们很难找到它们之间的直接映射关系来减少领域之间的差异。因此，当我们将在一个领域中训练的分类器应用于其他领域时，情感分类器的准确性会急剧下降。本文提出了一种基于关联规则的跨领域情感分类方法--词对齐方法（WAAR）。该方法通过学习同一领域中的领域共享词和领域特定词之间的强关联规则，建立不同领域中的领域特定词之间的间接映射关系。通过这种方式，可以在一定程度上减少源域和目标域之间的差异，并且可以训练出更准确的跨域分类器。在Amazon（R）数据集上的实验结果表明了该方法在提高跨领域情感分类性能方面的有效性。
Automatic classification of sentiment data (e.g., reviews, blogs) has many applications in enterprise user management systems, and can help us understand people's attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules (WAAR) for cross-domain sentiment classification, which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon (R) datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.