Opinion mining from customer reviews has become pervasive in recent years.
Sentences in reviews, however, are usually classified independently, even
though they form part of a review's argumentative structure. Intuitively,
sentences in a review build and elaborate upon each other; knowledge of the
review structure and sentential context should thus inform the classification
of each sentence. We demonstrate this hypothesis for the task of aspect-based
sentiment analysis by modeling the interdependencies of sentences in a review
with a hierarchical bidirectional LSTM. We show that the hierarchical model
outperforms two non-hierarchical baselines, obtains results competitive with
the state-of-the-art, and outperforms the state-of-the-art on five
multilingual, multi-domain datasets without any hand-engineered features or
external resources.

通过建模评论中的句子之间的相互依赖关系，我们提出了一种基于分层双向 LSTM 的情感分析方法，在五个多语种、多领域的数据集上取得了竞争状态下最先进结果，并优于其它两种基线模型，而且不需要使用手工特征或外部资源。