Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake -- to our knowledge for the first time -- an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two large-scale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus.

本文介绍了一个关于在情感分析中领域适应的研究。我们对领域相似度度量进行了广泛的研究，提出了新的表示方法、度量方法和数据选择。在推文和评论的大规模多领域适应设置中，我们评估了所提出的方法，并证明了它们一致地优于强随机和平衡的基线方法，而我们的选择策略优于实例级选择，并在大型评论语料库上获得了最佳分数。

多域情感分析的数据选择策略