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.
Therefore, He et al. REF explore two approaches for aspect-level sentiment classification, pretraining and multitask learning based on LSTM and attention, which transfer knowledge from document-level data obtained less expensive.
Exploiting Document Knowledge for Aspect-level Sentiment Classification
ArXiv
ArXiv
Computer Science