Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from documentlevel data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014 SemEval , 2015 SemEval , and 2016 , and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.
Due to the difficulty of annotating, existing public datasets for aspect-level sentiment classification are relatively small, which greatly limits the effectiveness of neural models REF .
Exploiting Document Knowledge for Aspect-level Sentiment Classification
ArXiv
ArXiv
Computer Science