Sentiment analysis (SA) is an important research area in cognitive computation—thus, in-depth studies of patterns of sentiment analysis are necessary. At present, rich-resource data-based SA has been well-developed, while the more challenging and practical multi-source unsupervised SA (i.e., a target-domain SA by transferring from multiple source domains) is seldom studied. The challenges behind this problem mainly locate in the lack of supervision information, the semantic gaps among domains (i.e., domain shifts), and the loss of knowledge. However, existing methods either lack the distinguishable capacity of the semantic gaps among domains or lose private knowledge. To alleviate these problems, we propose a two-stage domain adaptation framework. In the first stage, a multi-task methodology-based shared-private architecture is employed to explicitly model the domain-common features and the domain-specific features for the labeled source domains. In the second stage, two elaborate mechanisms are embedded in the shared-private architecture to transfer knowledge from multiple source domains. The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain. And the second mechanism is a target-oriented ensemble (TOE) method, in which knowledge is transferred through a well-designed ensemble method. Extensive experiment evaluations verify that the performance of the proposed framework outperforms unsupervised state-of-the-art competitors. What can be concluded from the experiments is that transferring from very different distributed source domains may degrade the target-domain performance, and it is crucial to choose proper source domains to transfer from.

情感分析（SA）是认知计算中的一个重要研究领域，因此有必要对情感分析模式进行深入研究。目前，基于丰富资源数据的SA已经得到很好的发展，而更具挑战性和实用性的多源无监督SA（即从多个源域转移而来的目标域SA）很少被研究。这个问题背后的挑战主要在于监督信息的缺乏、领域之间的语义鸿沟（即领域转移）以及知识的丢失。然而，现有方法要么缺乏域间语义差距的可区分能力，要么丢失私有知识。为了缓解这些问题，我们提出了一个两阶段的域适应框架。在第一阶段，采用基于多任务方法的共享-私有架构来显式建模标记源域的域公共特征和域特定特征。在第二阶段，两个精心设计的机制嵌入到共享私有架构中，以从多个源域传输知识。第一种机制是选择性域适应（SDA ) 方法，从最近的源域转移知识。第二种机制是面向目标的集成（TOE）方法，其中知识通过精心设计的集成方法进行转移。广泛的实验评估验证了所提出框架的性能优于无监督的最先进的竞争对手。从实验中可以得出的结论是，从非常不同的分布式源域转移可能会降低目标域的性能，选择合适的源域进行转移至关重要。