Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1330-1338.DOI: 10.11772/j.issn.1001-9081.2021040654
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Received:
2021-04-25
Revised:
2021-07-10
Accepted:
2021-07-14
Online:
2022-06-11
Published:
2022-05-10
Contact:
Liqing QIU
About author:
QIU Liqing, born in 1978, Ph. D., associate professor. Her research interests include social network,data mining.Supported by:
通讯作者:
仇丽青
作者简介:
仇丽青(1978—),女,山东德州人,副教授,博士,主要研究方向:社交网络、数据挖掘 qiuliqing2019@163.com基金资助:
CLC Number:
Liqing QIU, Fushuai QU. Emotional map of emergency based on sentiment analysis and influence evaluation[J]. Journal of Computer Applications, 2022, 42(5): 1330-1338.
仇丽青, 曲福帅. 基于情感分析和影响力评估的突发事件情感图谱[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1330-1338.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040654
参数 | 值 |
---|---|
词向量维度 | 100 |
卷积核大小 | 3 |
丢弃率 | 0.6 |
Bi-LSTM隐藏单元个数 | 128 |
优化器 | Adam Optimizer |
早停法耐心值 | 50 |
学习率 | 0.001 |
批次大小 | 16 |
Tab. 1 Experimental parameter setting
参数 | 值 |
---|---|
词向量维度 | 100 |
卷积核大小 | 3 |
丢弃率 | 0.6 |
Bi-LSTM隐藏单元个数 | 128 |
优化器 | Adam Optimizer |
早停法耐心值 | 50 |
学习率 | 0.001 |
批次大小 | 16 |
模型 | 情感倾向 | 准确率 | 召回率 | F1值 |
---|---|---|---|---|
Text-RNN | 积极 | 76.08 | 52.70 | 62.27 |
消极 | 79.20 | 91.57 | 84.98 | |
Text-RCNN | 积极 | 78.75 | 65.55 | 71.54 |
消极 | 83.86 | 91.00 | 87.28 | |
Text-RNN+Attention | 积极 | 74.32 | 65.74 | 69.76 |
消极 | 83.54 | 88.45 | 85.92 | |
FastText | 积极 | 80.68 | 52.89 | 63.89 |
消极 | 79.61 | 93.56 | 86.03 | |
DPCNN | 积极 | 76.50 | 64.25 | 69.84 |
消极 | 83.19 | 89.96 | 86.44 | |
Transformer | 积极 | 74.69 | 67.60 | 70.97 |
消极 | 84.28 | 88.35 | 86.27 | |
本文模型 | 积极 | 78.98 | 66.48 | 72.19 |
消极 | 84.22 | 91.00 | 87.48 |
Tab. 2 Experimental results of different models on hotel review dataset
模型 | 情感倾向 | 准确率 | 召回率 | F1值 |
---|---|---|---|---|
Text-RNN | 积极 | 76.08 | 52.70 | 62.27 |
消极 | 79.20 | 91.57 | 84.98 | |
Text-RCNN | 积极 | 78.75 | 65.55 | 71.54 |
消极 | 83.86 | 91.00 | 87.28 | |
Text-RNN+Attention | 积极 | 74.32 | 65.74 | 69.76 |
消极 | 83.54 | 88.45 | 85.92 | |
FastText | 积极 | 80.68 | 52.89 | 63.89 |
消极 | 79.61 | 93.56 | 86.03 | |
DPCNN | 积极 | 76.50 | 64.25 | 69.84 |
消极 | 83.19 | 89.96 | 86.44 | |
Transformer | 积极 | 74.69 | 67.60 | 70.97 |
消极 | 84.28 | 88.35 | 86.27 | |
本文模型 | 积极 | 78.98 | 66.48 | 72.19 |
消极 | 84.22 | 91.00 | 87.48 |
模型 | 情感倾向 | 准确率 | 召回率 | F1值 |
---|---|---|---|---|
Text-RNN | 积极 | 78.75 | 77.63 | 78.19 |
消极 | 78.38 | 79.48 | 78.93 | |
Text-RCNN | 积极 | 78.03 | 79.61 | 79.86 |
消极 | 81.26 | 77.45 | 79.31 | |
Text-RNN+Attention | 积极 | 77.10 | 80.33 | 78.68 |
消极 | 79.90 | 76.63 | 78.23 | |
FastText | 积极 | 48.61 | 18.09 | 26.37 |
消极 | 50.31 | 81.27 | 62.15 | |
DPCNN | 积极 | 86.03 | 69.83 | 77.09 |
消极 | 75.04 | 88.89 | 81.38 | |
Transformer | 积极 | 78.24 | 78.46 | 78.35 |
消极 | 78.84 | 78.61 | 78.73 | |
本文模型 | 积极 | 79.31 | 83.97 | 81.57 |
消极 | 81.46 | 77.60 | 79.49 |
Tab. 3 Experimental results of different models on e-commerce review dataset
模型 | 情感倾向 | 准确率 | 召回率 | F1值 |
---|---|---|---|---|
Text-RNN | 积极 | 78.75 | 77.63 | 78.19 |
消极 | 78.38 | 79.48 | 78.93 | |
Text-RCNN | 积极 | 78.03 | 79.61 | 79.86 |
消极 | 81.26 | 77.45 | 79.31 | |
Text-RNN+Attention | 积极 | 77.10 | 80.33 | 78.68 |
消极 | 79.90 | 76.63 | 78.23 | |
FastText | 积极 | 48.61 | 18.09 | 26.37 |
消极 | 50.31 | 81.27 | 62.15 | |
DPCNN | 积极 | 86.03 | 69.83 | 77.09 |
消极 | 75.04 | 88.89 | 81.38 | |
Transformer | 积极 | 78.24 | 78.46 | 78.35 |
消极 | 78.84 | 78.61 | 78.73 | |
本文模型 | 积极 | 79.31 | 83.97 | 81.57 |
消极 | 81.46 | 77.60 | 79.49 |
网络 | 节点数 | 边数 | 最大度 | 平均度 | 同配性 |
---|---|---|---|---|---|
Karate | 34 | 78 | 17 | 4.558 2 | |
Copperfield | 112 | 425 | 49 | 7.589 3 | |
USAir97 | 332 | 2 126 | 139 | 12.000 0 | |
Euroroad | 1 174 | 1 417 | 10 | 2.414 0 | 0.126 7 |
PowerGrid | 4 941 | 6 594 | 19 | 2.669 1 | 0.003 5 |
Tab. 4 Statistics of social network datasets
网络 | 节点数 | 边数 | 最大度 | 平均度 | 同配性 |
---|---|---|---|---|---|
Karate | 34 | 78 | 17 | 4.558 2 | |
Copperfield | 112 | 425 | 49 | 7.589 3 | |
USAir97 | 332 | 2 126 | 139 | 12.000 0 | |
Euroroad | 1 174 | 1 417 | 10 | 2.414 0 | 0.126 7 |
PowerGrid | 4 941 | 6 594 | 19 | 2.669 1 | 0.003 5 |
网络 | 算法 | |||||
---|---|---|---|---|---|---|
BC | CC | DC | HX | KS | WDK | |
Karate | 0.775 4 | 0.899 3 | 0.707 9 | 0.576 6 | 0.495 8 | 0.964 7 |
Copperfiled | 0.978 9 | 0.983 7 | 0.866 1 | 0.811 0 | 0.599 0 | 0.991 2 |
USAir97 | 0.697 0 | 0.989 2 | 0.858 6 | 0.835 5 | 0.811 4 | 0.995 9 |
Euroroad | 0.937 4 | 0.998 8 | 0.444 2 | 0.253 4 | 0.212 9 | 0.999 4 |
PowerGrid | 0.831 9 | 0.999 8 | 0.592 7 | 0.393 0 | 0.246 0 | 0.999 9 |
Tab. 5 M-function value of node influence algorithm
网络 | 算法 | |||||
---|---|---|---|---|---|---|
BC | CC | DC | HX | KS | WDK | |
Karate | 0.775 4 | 0.899 3 | 0.707 9 | 0.576 6 | 0.495 8 | 0.964 7 |
Copperfiled | 0.978 9 | 0.983 7 | 0.866 1 | 0.811 0 | 0.599 0 | 0.991 2 |
USAir97 | 0.697 0 | 0.989 2 | 0.858 6 | 0.835 5 | 0.811 4 | 0.995 9 |
Euroroad | 0.937 4 | 0.998 8 | 0.444 2 | 0.253 4 | 0.212 9 | 0.999 4 |
PowerGrid | 0.831 9 | 0.999 8 | 0.592 7 | 0.393 0 | 0.246 0 | 0.999 9 |
网络 | τ(σ,WDK) | |||||
---|---|---|---|---|---|---|
Karate | 0.556 1 | 0.702 3 | 0.830 7 | 0.616 8 | 0.572 2 | 0.862 7 |
Copperfield | 0.638 0 | 0.851 2 | 0.903 0 | 0.831 4 | 0.725 7 | 0.916 5 |
USAir97 | 0.517 8 | 0.804 8 | 0.916 8 | 0.763 0 | 0.759 3 | 0.943 1 |
Euroroad | 0.393 4 | 0.608 0 | 0.489 5 | 0.429 1 | 0.408 8 | 0.724 1 |
PowerGrid | 0.377 4 | 0.349 2 | 0.488 5 | 0.444 2 | 0.346 8 | 0.682 9 |
Tab. 6 Kendall coefficients of node influence algorithms
网络 | τ(σ,WDK) | |||||
---|---|---|---|---|---|---|
Karate | 0.556 1 | 0.702 3 | 0.830 7 | 0.616 8 | 0.572 2 | 0.862 7 |
Copperfield | 0.638 0 | 0.851 2 | 0.903 0 | 0.831 4 | 0.725 7 | 0.916 5 |
USAir97 | 0.517 8 | 0.804 8 | 0.916 8 | 0.763 0 | 0.759 3 | 0.943 1 |
Euroroad | 0.393 4 | 0.608 0 | 0.489 5 | 0.429 1 | 0.408 8 | 0.724 1 |
PowerGrid | 0.377 4 | 0.349 2 | 0.488 5 | 0.444 2 | 0.346 8 | 0.682 9 |
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