Sentiment Lexicon Enhanced Attention-Based LSTM for Sentiment Classification


  • Zeyang Lei Tsinghua University
  • Yujiu Yang Tsinghua University
  • Min Yang Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences



sentiment classification, multi-head attention, sentiment lexicon


Deep neural networks have gained great success recently for sentiment classification. However, these approaches do not fully exploit the linguistic knowledge. In this paper, we propose a novel sentiment lexicon enhanced attention-based LSTM (SLEA-LSTM) model to improve the performance of sentence-level sentiment classification. Our method successfully integrates sentiment lexicon into deep neural networks via single-head or multi-head attention mechanisms. We conduct extensive experiments on MR and SST datasets. The experimental results show that our model achieved comparable or better performance than the state-of-the-art methods.




How to Cite

Lei, Z., Yang, Y., & Yang, M. (2018). Sentiment Lexicon Enhanced Attention-Based LSTM for Sentiment Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).