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Complaint Classification Using Hybrid-Attention GRU Neural Network

  • Shuyang Wang
  • Bin WuEmail author
  • Bai Wang
  • Xuesong Tong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

Recently, a growing number of customers tend to complain about the services of different enterprises on the Internet to express their dissatisfaction. The correct classification of complaint texts is fairly important for enterprises to improve the efficiency of transaction processing. However, the existing literature lacks research on complaint texts. Most previous approaches of text classification fail to take advantage of the information of specific characters and negative emotions in complaint texts. Besides, some grammatical and semantic errors caused by violent mood swings of customers are another challenge. To address the problems, a novel model based on hybrid-attention GRU neural network (HATT-GRU) is proposed for complaint classification. The model constructs text vectors at character level, and it is able to extract sentiment features in complaint texts. Then a hybrid-attention mechanism is proposed to learn the importance of each character and sentiment feature, so that the model can focus on the features that contribute more to text classification. Finally, experiments are conducted on two complaint datasets from different industries. Experiments show that our model can achieve state-of-the-art results on both Chinese and English datasets compared to several text classification baselines.

Keywords

Text classification Recurrent neural network Attention 

Notes

Acknowledgments

This work is supported by the National Key R&D Program of China (No. 2018YFC0831500) and the National Social Science Foundation of China under Grant 16ZDA055.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

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