Attention Based LSTM for Target Dependent Sentiment Classification

Min Yang, Wenting Tu, Jingxuan Wang, Fei Xu, Xiaojun Chen

AAAI 2017 pp. 5013-5014

doi:10.1609/AAAI.V31I1.11061 /aaai/2017/yang2017aaai-attention/

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.

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Cite

Text

Yang et al. "Attention Based LSTM for Target Dependent Sentiment Classification." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11061

Markdown

[Yang et al. "Attention Based LSTM for Target Dependent Sentiment Classification." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/yang2017aaai-attention/) doi:10.1609/AAAI.V31I1.11061

BibTeX

@inproceedings{yang2017aaai-attention,
  title     = {{Attention Based LSTM for Target Dependent Sentiment Classification}},
  author    = {Yang, Min and Tu, Wenting and Wang, Jingxuan and Xu, Fei and Chen, Xiaojun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5013-5014},
  doi       = {10.1609/AAAI.V31I1.11061},
  url       = {https://mlanthology.org/aaai/2017/yang2017aaai-attention/}
}