  Most of the existing pre-trained language representation models neglect to
consider the linguistic knowledge of texts, which can promote language
understanding in NLP tasks. To benefit the downstream tasks in sentiment
analysis, we propose a novel language representation model called SentiLARE,
which introduces word-level linguistic knowledge including part-of-speech tag
and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We
first propose a context-aware sentiment attention mechanism to acquire the
sentiment polarity of each word with its part-of-speech tag by querying
SentiWordNet. Then, we devise a new pre-training task called label-aware masked
language model to construct knowledge-aware language representation.
Experiments show that SentiLARE obtains new state-of-the-art performance on a
variety of sentiment analysis tasks.
