Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3527-3533.DOI: 10.11772/j.issn.1001-9081.2022010073
• ChinaService 2021 • Previous Articles
Received:
2022-01-20
Revised:
2022-04-15
Accepted:
2022-04-21
Online:
2022-05-17
Published:
2022-11-10
Contact:
Huiping LIN
About author:
ZHANG Jiaju, born in 1996, M. S. candidate. Her research interests include data mining, service quality analysis, natural language processingSupported by:
通讯作者:
林慧苹
作者简介:
张家菊(1996—),女,吉林榆树人,硕士研究生,主要研究方向:数据挖掘、服务质量分析、自然语言处理基金资助:
CLC Number:
Jiaju ZHANG, Huiping LIN. Product and service quality analysis based on customer service dialogues[J]. Journal of Computer Applications, 2022, 42(11): 3527-3533.
张家菊, 林慧苹. 基于客服对话的产品和服务质量分析[J]. 《计算机应用》唯一官方网站, 2022, 42(11): 3527-3533.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022010073
产品 | 服务 | ||
---|---|---|---|
编号 | 评价要素 | 编号 | 评价要素 |
产品颜色 | 咨询响应性 | ||
产品气味 | 系统稳定性 | ||
产品功效 | 发货及时性 | ||
产品用法 | 发货正确性 | ||
生产日期 | 运输时效性 | ||
保质期 | 配送服务水平 | ||
产品价格 | 货品完好性 | ||
产品规格 | 退换货响应性 | ||
产品包装 | 补偿性 |
Tab. 1 Product and service evaluation factors definition for a flagship shop
产品 | 服务 | ||
---|---|---|---|
编号 | 评价要素 | 编号 | 评价要素 |
产品颜色 | 咨询响应性 | ||
产品气味 | 系统稳定性 | ||
产品功效 | 发货及时性 | ||
产品用法 | 发货正确性 | ||
生产日期 | 运输时效性 | ||
保质期 | 配送服务水平 | ||
产品价格 | 货品完好性 | ||
产品规格 | 退换货响应性 | ||
产品包装 | 补偿性 |
产品 | 服务 | ||
---|---|---|---|
编号 | 对话数量 | 编号 | 对话数量 |
69 | 105 | ||
437 | 24 | ||
698 | 1 260 | ||
1 444 | 3 646 | ||
48 | 4 203 | ||
72 | 2 101 | ||
3 372 | 4 889 | ||
251 | 2 481 | ||
42 | 132 |
Tab. 2 Dialogue number of each evaluation factor
产品 | 服务 | ||
---|---|---|---|
编号 | 对话数量 | 编号 | 对话数量 |
69 | 105 | ||
437 | 24 | ||
698 | 1 260 | ||
1 444 | 3 646 | ||
48 | 4 203 | ||
72 | 2 101 | ||
3 372 | 4 889 | ||
251 | 2 481 | ||
42 | 132 |
模型 | 精确率 | 召回率 | F1值 |
---|---|---|---|
BERT | 0.911 6 | 0.902 4 | 0.906 8 |
BiLSTM | 0.892 3 | 0.886 5 | 0.889 3 |
TextCNN | 0.878 2 | 0.891 8 | 0.884 9 |
DialogueRNN | 0.921 3 | 0.914 7 | 0.917 9 |
Tab. 3 Comparison of experimental results of different sentiment analysis models
模型 | 精确率 | 召回率 | F1值 |
---|---|---|---|
BERT | 0.911 6 | 0.902 4 | 0.906 8 |
BiLSTM | 0.892 3 | 0.886 5 | 0.889 3 |
TextCNN | 0.878 2 | 0.891 8 | 0.884 9 |
DialogueRNN | 0.921 3 | 0.914 7 | 0.917 9 |
对话示例 | 满意度得分 |
---|---|
“给宝宝用的,味道有些重了” | 0.762 7 |
“这个味道怎么这么刺鼻啊” | 0.982 5 |
“这两个味道的功效有区别吗?” | 0.001 3 |
Tab. 4 Examples of dialogue sentiment score calculation result
对话示例 | 满意度得分 |
---|---|
“给宝宝用的,味道有些重了” | 0.762 7 |
“这个味道怎么这么刺鼻啊” | 0.982 5 |
“这两个味道的功效有区别吗?” | 0.001 3 |
评价要素 | 重要性 | 满意度 | 评价要素 | 重要性 | 满意度 |
---|---|---|---|---|---|
0.002 9 | 0.576 2 | 0.004 4 | 0.624 6 | ||
0.018 1 | 0.743 9 | 0.001 0 | 0.821 7 | ||
0.028 9 | 0.400 3 | 0.052 2 | 0.423 6 | ||
0.059 8 | 0.373 2 | 0.150 9 | 0.748 1 | ||
0.002 0 | 0.676 3 | 0.174 0 | 0.688 5 | ||
0.002 9 | 0.183 7 | 0.086 9 | 0.763 9 | ||
0.139 6 | 0.801 2 | 0.202 4 | 0.916 1 | ||
0.010 4 | 0.102 8 | 0.102 7 | 0.548 0 | ||
0.001 7 | 0.410 6 | 0.005 5 | 0.310 6 |
Tab. 5 Calculation results of importance and performance of each evaluation factor
评价要素 | 重要性 | 满意度 | 评价要素 | 重要性 | 满意度 |
---|---|---|---|---|---|
0.002 9 | 0.576 2 | 0.004 4 | 0.624 6 | ||
0.018 1 | 0.743 9 | 0.001 0 | 0.821 7 | ||
0.028 9 | 0.400 3 | 0.052 2 | 0.423 6 | ||
0.059 8 | 0.373 2 | 0.150 9 | 0.748 1 | ||
0.002 0 | 0.676 3 | 0.174 0 | 0.688 5 | ||
0.002 9 | 0.183 7 | 0.086 9 | 0.763 9 | ||
0.139 6 | 0.801 2 | 0.202 4 | 0.916 1 | ||
0.010 4 | 0.102 8 | 0.102 7 | 0.548 0 | ||
0.001 7 | 0.410 6 | 0.005 5 | 0.310 6 |
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