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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (1): 128-137    DOI: 10.11925/infotech.2096-3467.2022.0258
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Classifying Customer Complaints Based on Multi-head Co-attention Mechanism
Wang jinzheng1,Yang Ying1,2(),Yu Bengong1,2
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization & Intelligent Decision-making of Ministry of Education, Hefei 230009, China
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Abstract  

[Objective] This paper tries to improve the insufficient learning of the relationship between features in the traditional text classification model. [Methods] We developed a text classification model for customer complaints based on multi-head co-attention mechanism. Firstly, we used the BERT pre-training model to create text vectors. Then, we constructed the Text-CNN and BiLSTM multi-channel feature networks to extract the local and global features of the complaints. Finally, we used the collaborative attention mechanism to learn the relationship between the local and global features to classify complaints. [Results] We examined our model with a public dataset (THUCNews) and its accuracy reached 97.25%, while the accuracy on the telecom customer complaint dataset reached 86.20%. Compared with the single channel baseline model with the best performance and the multi-channel model without feature interaction, the accuracy of the proposed model on telecom customer complaint dataset was improved by 0.54% and 0.35%, respectively. [Limitations] We only examined the interaction between the two features. With the small-scale telecom customer complaint dataset, the classification of some complaint is not satisfactory. [Conclusions] Multi-channel feature extraction network can enrich text information and fully extract text features. Co-attention mechanism can effectively learn the relationship between text features, and improve the model’s classification performance.
Key words： Text Classification      Multi-head Co-attention Mechanism      Customer Complaints     
Received: 25 March 2022      Published: 16 February 2023
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(72071061)
Corresponding Authors: Yang Ying，ORCID：0000-0002-9912-3443，E-mail： yangying@hfut.edu.cn。   
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jinzheng Wang
Ying Yang
Bengong Yu

Cite this article:

Wang jinzheng, Yang Ying, Yu Bengong. Classifying Customer Complaints Based on Multi-head Co-attention Mechanism. Data Analysis and Knowledge Discovery, 2023, 7(1): 128-137.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0258     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I1/128

Customer Complaint Text Classification Model Based on a Multi-head Co-attention Mechanism
Structure of Text-CNN
Structure of LSTM
Multi-head Co-attention Mechanism
Co-attention Mechanism
实验环境 参数配置
操作系统 Windows 10
IDE Jupyter Notebook 6.0.1
PyTorch 1.8.0
GPU RTX3060（12GB）
Python 3.8.5
Experimental Environment Parameters
Relationship Between Epoch and Loss
参数名称 参数值
词向量维度 768
句子最大长度 500
Epochs 50
优化器 SGD
损失函数 CrossEntropy
BiLSTM隐层大小 256
Text-CNN卷积核大小 3、5、7
Text-CNN卷积核通道数 256、256、256
最大池化尺寸 490
学习率 0.001
漏失率 0.5
注意力层头数 8
Parameter Setting
类别 标签 训练集 测试集 总计
第1类 0 1 441 167 1 608
第2类 1 1 666 185 1 851
第3类 2 3 065 338 3 403
第4类 3 1 396 151 1 547
总计 7 568 841 8 409
Statistics of Telecom Customer Complaint Data
模型 类别 THUCNews 电信客户投诉数据集
准确率/% F1/% 准确率/% F1/%
NLSTM 单通道 93.39[7] 93.39[7] 82.75 82.73
RCNN 单通道 95.69 95.67 83.71 83.71
LSTM+att 单通道 94.87[5] 94.85[5] 84.24 84.23
BiLSTM+att 单通道 95.05[5] 95.02[5] 85.32 85.32
BiLSTM+max-pooling 单通道 94.16 94.14 85.42 85.40
CNLSTM 单通道 96.87[7] 96.86[7] 85.66 85.60
组合-CNN 多通道 95.57[10] 95.55[10] 84.54 84.34
CFC-LSTM-multi 多通道 96.21[5] 96.20[5] 85.75 85.75
本文方法 多通道 97.25 97.24 86.20 86.20
Experimental Results of Comparison Methods
模型 THUCNews 电信客户投诉数据集
准确率（%） F1（%） 准确率（%） F1（%）
CNN-LSTM-Co 96.83 96.83 86.05 86.04
本文方法 97.25 97.24 86.20 86.20
Comparison with Different Attention Mechanism
模型 THUCNews 电信客户投诉数据集
准确率（%） F1（%） 准确率（%） F1（%）
Text-CNN 92.37 92.35 85.37 85.36
BiLSTM 94.07 94.07 85.21 85.15
Text-CNN+BiLSTM 94.63 94.62 85.85 85.82
本文方法 97.25 97.24 86.20 86.20
Comparison Results of Ablation Experiment
Confusion Matrix of Complaint Dataset
Confusion Matrix of THUCNews Dataset
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