Learning Unsupervised Semantic Document Representation for Fine-grained
  Aspect-based Sentiment Analysis
Hao-Ming Fu Pu-Jen Cheng
Document representation is the core of many NLP tasks on machine
understanding. A general representation learned in an unsupervised manner
reserves generality and can be used for various applications. In practice,
sentiment analysis (SA) has been a challenging task that is regarded to be
deeply semantic-related and is often used to assess general representations.
Existing methods on unsupervised document representation learning can be
separated into two families: sequential ones, which explicitly take the
ordering of words into consideration, and non-sequential ones, which do not
explicitly do so. However, both of them suffer from their own weaknesses. In
this paper, we propose a model that overcomes difficulties encountered by both
families of methods. Experiments show that our model outperforms
state-of-the-art methods on popular SA datasets and a fine-grained aspect-based
SA by a large margin.