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Target Information Fusion Based on Memory Network for Aspect-Level Sentiment Classification

  • Zhaochuan Wei
  • Jun Peng
  • Xiaodong CaiEmail author
  • Guangming He
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

Aspect-level sentiment classification is a fine-grained task that provides more complete and deeper analysis results. Attention networks are widely used for aspect-level sentiment classification task. However, when multiple target words in a sentence contain opposite sentiments or the expressions of different targets are similar, the network tends to perform poorly. Our studies find that the method of averaging a sentence or target words weakens the capacity of key words. A target information fusion memory network is proposed to solve this problem in this paper. Firstly, the feature of sentences is extracted through a Bi-LSTM network. Then, the feature of target is extracted incorporated into the sentence feature extracted. Then, the memory information of the specific target is formed by the position coding. Finally, the recurrent attention network is utilized to extract the sentiment expression from the memory. Compare with IAN, the method proposed achieves 1.5% and 1.9% accuracy improvement on SemEvil2014 restaurant dataset and self-defined Chinese mobile phone dataset, respectively. A further extend experiment proves that the proposed method can effectively improve the performance in the case of complex sentences.

Keywords

Aspect-level sentiment classification Interactive attention neural network Memory network Information fusion 

Notes

Acknowledgements

Our work is supported by Key Research and Development Projects of Xinjiang Autonomous Region in 2018 (No. 2018B03022-1 and No. 2018B03022-2), Innovation Project of GUET Graduate Education (No. 2018YJCX38 and No. 2017YJCX38) and Key Research and Guangxi Director Fund of the Key Laboratory of Wireless Broadband Communication and Signal Processing (No. GXKL0614107).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhaochuan Wei
    • 1
  • Jun Peng
    • 1
  • Xiaodong Cai
    • 1
    Email author
  • Guangming He
    • 2
  1. 1.Guilin University of Electronic TechnologyGuilinChina
  2. 2.China Comservice Public Information Industry Co., Ltd.UrumqiChina

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