  Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect
extraction) and the sentiment polarities (sentiment detection) towards them.
Both the previous pipeline and integrated methods fail to precisely model the
innate connection between these two objectives. In this paper, we propose a
novel dynamic heterogeneous graph to jointly model the two objectives in an
explicit way. Both the ordinary words and sentiment labels are treated as nodes
in the heterogeneous graph, so that the aspect words can interact with the
sentiment information. The graph is initialized with multiple types of
dependencies, and dynamically modified during real-time prediction. Experiments
on the benchmark datasets show that our model outperforms the state-of-the-art
models. Further analysis demonstrates that our model obtains significant
performance gain on the challenging instances under multiple-opinion aspects
and no-opinion aspect situations.
