Emotional map of emergency based on sentiment analysis and influence evaluation

doi:10.11772/j.issn.1001-9081.2021040654

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

Emotional map of emergency based on sentiment analysis and influence evaluation

Liqing QIU(), Fushuai QU

College of Computer Science and Engineering，Shandong University of Science and Technology，Qingdao Shandong 266590，China

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.
QU Fushuai，born in 1996，M. S. candidate. His research interests include social network，sentiment analysis.
Supported by:
National Natural Science Foundation of China(71772107);Shandong Natural Science Foundation(ZR2020MF044);Digital Shandong Research Project of Shandong Social Science Plan(21CSDJ48);Qingdao Social Science Planning Project(QDSKL1801103)

基于情感分析和影响力评估的突发事件情感图谱

仇丽青(), 曲福帅

山东科技大学计算机科学与工程学院，山东青岛 266590

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
曲福帅（1996—），男，山东潍坊人，硕士研究生，主要研究方向：社交网络、情感分析。
基金资助:
国家自然科学基金资助项目(71772107);山东省自然科学基金资助项目(ZR2020MF044);山东省社会科学规划数字山东研究专项(21CSDJ48);青岛市社科规划项目(QDSKL1801103)

Abstract

Abstract:

Aiming the spread of negative network public opinions in emergencies， a research method of emotional map of emergency based on sentiment analysis and influence evaluation was proposed. In the proposed method， a sentiment analysis model based on multi-head self-attention mechanism and Bi-directional Long Short-Term Memory network （Bi-LSTM） was proposed to evaluate website users’ emotional tendencies. Meanwhile， a point influence evaluation algorithm combining weighted degree and K-shell value was proposed to measure users’ influences. Based on the above models， the emotional map of emergency was constructed， which effectively improved the accuracy and scientificity of the emotional map. Taking “7.7 Anshun Bus Falling into Lake Incident” as an example， the life cycle of an emergency was divided into four stages such as outbreak stage， spread stage， maturity stage and decline stage， which were used to separately generate the emotional maps for visualization analysis. Experimental results show that， the F1-score of the proposed sentiment analysis model on the hotel review dataset is 9.92 percentage points and 2.5 percentage points higher than that of Recurrent Neural Networks for Text Classification （Text-RNN） model in positive and negative aspects respectively. On the Karate network， the discrimination and accuracy of the proposed influence evaluation algorithm are 46.89 percentage points and 29.05 percentage points higher than those of the K-shell algorithm respectively. By building the emotional map based on social networks， relevant department can find the opinion leaders and their tendencies， thereby grasping the development trend of online public opinion， and reducing the influence of negative emotions on society.

Key words: social network, sentiment analysis, opinion leader, emotional map, public opinion monitoring

摘要：

针对突发事件中负面网络舆情传播的问题，提出了一种基于情感分析和影响力评估的突发事件情感图谱研究方法。提出了一种基于多头自注意力机制和双向长短期记忆网络（Bi-LSTM）的情感分析模型来计算网站用户的情感倾向，并提出了一种融合加权度与K-shell值的节点影响力评估算法来评估用户的影响力，从而综合构建突发事件的情感图谱，有效提高了情感图谱的准确性和科学性。以“7.7安顺公交车坠湖事件”为例，将突发事件的生命周期划分为爆发期、蔓延期、成熟期和衰退期四个阶段，分别生成情感图谱进行可视化分析。实验结果表明，在酒店评论数据集上，所提出的情感分析模型的F1值在积极和消极方面比文本循环神经网络（Text-RNN）模型分别提升了9.92个百分点和2.5个百分点；在Karate网络上，所提影响力评估算法的区分度和准确性比K-shell算法分别提升了46.89个百分点和29.05个百分点。构建基于社交网络的情感图谱有助于相关部门发现意见领袖及其情感倾向，从而把握网络舆情的发展趋势，并降低消极情感对社会造成的影响。

关键词: 社交网络, 情感分析, 意见领袖, 情感图谱, 舆情监测

CLC Number:

TP391

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.

Figures/Tables 12

Fig. 1 LSTM model structure

Fig. 2 Self-attention based Bi-LSTM model structure

Fig. 3 Emotional map structure based on social network

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

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
Text-RNN	消极	79.20	91.57	84.98
Text-RCNN	积极	78.75	65.55	71.54
Text-RCNN	消极	83.86	91.00	87.28
Text-RNN+Attention	积极	74.32	65.74	69.76
Text-RNN+Attention	消极	83.54	88.45	85.92
FastText	积极	80.68	52.89	63.89
FastText	消极	79.61	93.56	86.03
DPCNN	积极	76.50	64.25	69.84
DPCNN	消极	83.19	89.96	86.44
Transformer	积极	74.69	67.60	70.97
Transformer	消极	84.28	88.35	86.27
本文模型	积极	78.98	66.48	72.19
本文模型	消极	84.22	91.00	87.48

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
Text-RNN	消极	78.38	79.48	78.93
Text-RCNN	积极	78.03	79.61	79.86
Text-RCNN	消极	81.26	77.45	79.31
Text-RNN+Attention	积极	77.10	80.33	78.68
Text-RNN+Attention	消极	79.90	76.63	78.23
FastText	积极	48.61	18.09	26.37
FastText	消极	50.31	81.27	62.15
DPCNN	积极	86.03	69.83	77.09
DPCNN	消极	75.04	88.89	81.38
Transformer	积极	78.24	78.46	78.35
Transformer	消极	78.84	78.61	78.73
本文模型	积极	79.31	83.97	81.57
本文模型	消极	81.46	77.60	79.49

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	$-$ 0.475 6
Copperfield	112	425	49	7.589 3	$-$ 0.129 4
USAir97	332	2 126	139	12.000 0	$-$ 0.207 9
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	$-$ 0.475 6
Copperfield	112	425	49	7.589 3	$-$ 0.129 4
USAir97	332	2 126	139	12.000 0	$-$ 0.207 9
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. 5 M-function value of node influence algorithm

网络	算法
网络	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. 6 Kendall coefficients of node influence algorithms

网络	$τ (σ, B C)$	$τ (σ, C C)$	$τ (σ, D C)$	$τ (σ, H X)$	$τ (σ, K S)$	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	$τ (σ, B C)$	$τ (σ, C C)$	$τ (σ, D C)$	$τ (σ, H X)$	$τ (σ, K S)$	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

Fig. 4 User status in each development stage of “7.7 Anshun Bus Falling into Lake Incident”

Fig. 5 Emotional map of “7.7 Anshun Bus Falling into Lake Incident”

Fig. 6 Word cloud of “7.7 Anshun Bus Falling into Lake Incident”

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Emotional map of emergency based on sentiment analysis and influence evaluation

基于情感分析和影响力评估的突发事件情感图谱

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