  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 the proper source
domains to transfer from.
