Attention Based LSTM for Target Dependent Sentiment Classification

Authors

  • Min Yang The University of Hong Kong
  • Wenting Tu The University of Hong Kong
  • Jingxuan Wang The University of Hong Kong
  • Fei Xu Chinese Academy of Sciences
  • Xiaojun Chen Shenzhen University

DOI:

https://doi.org/10.1609/aaai.v31i1.11061

Keywords:

sentiment classification, LSTM

Abstract

We present an attention-based bidirectional LSTM approach to improve the target-dependent sentiment classification. Our method learns the alignment between the target entities and the most distinguishing features. We conduct extensive experiments on a real-life dataset. The experimental results show that our model achieves state-of-the-art results.

AAAI-2017 Proceedings Cover

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Published

2017-02-12

How to Cite

Yang, M., Tu, W., Wang, J., Xu, F., & Chen, X. (2017). Attention Based LSTM for Target Dependent Sentiment Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11061

Issue

Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence

Section

Student Abstract Track