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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 68-79    DOI: 10.11925/infotech.2096-3467.2021.0339
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Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel
Han Pu1,2(),Zhang Wei1,Zhang Zhanpeng1,Wang Yuxin1,Fang Haoyu1
1School of Management, Nanjing University of Posts & Telecommunications, Nanjing 210003, China
2Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
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Abstract  

[Objective] This paper proposes a multi-channel MCMF-A model for Weibo posts based on feature fusion and attention mechanism, aiming to further explore the semantic information of public health emergency. [Methods] Firstly, we generated word vectors with Word2vec and FastText at the feature vector embedding level, which were merged with the vectors of part-of-speech features and position features. Secondly, we constructed multi-channel layer based on CNN and BiLSTM to extract local and global features of Weibo posts. Thirdly, we utilized the attention mechanism to extract important features of the texts. Finally, we merged the multi-channel output results, and used the softmax function for sentiment classification. [Results] We examined MCMF-A model with 42 384 Weibo posts on COVID-19. The F1 value of the proposed model reached 90.21%, which was 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models. [Limitations] More research is needed to expand the experiment data size to include more small and multi-modal information such as images and voices. [Conclusions] The proposed model could effectively conduct sentiment analysis with Weibo posts.
Key words： Multi-Channel      Feature Fusion      Deep Learning      Sentiment Analysis      Public Health Emergencies     
Received: 07 April 2021      Published: 23 December 2021
ZTFLH:  G350  
Fund:National Social Science Fund of China(17CTQ022);National Innovation Training Program for College Students(SZDG2020040);Jiangsu Graduate Research and Innovation Program Fund Project(KYCX20_0844)
Corresponding Authors: Han Pu,ORCID：0000-0001-5867-4292     E-mail: hanpu@njupt.edu.cn
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Pu Han
Wei Zhang
Zhanpeng Zhang
Yuxin Wang
Haoyu Fang

Cite this article:

Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel. Data Analysis and Knowledge Discovery, 2021, 5(11): 68-79.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0339     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/68

Experimental Flowchart
情感词名称 说明 词性标注
Positive Comment Words 正面评价词 PC
Positive Sentiment Words 正面情感词 PS
Negative Comment Words 负面评价词 NC
Negative Sentiment Words 负面情感词 NS
Degree Words 程度词 ADV
Negative Words 否定词 INVER
POS Tagging Rules
Multi-Feature Vector Fusion Process
Neural Network Structure Based on WMF
实验参数名称 说明 参数值
Max Length of Sentences
Size of Word Vector
Size of Sentiment Feature Vector
Size of Position Feature Vector
Batch Size
Window Size
Number of Feature Map
Hidden Size of BiLSTM
epochs		 最大文本序列长度
词向量的维度
词性特征向量的维度
位置特征向量的维度
每批数据量的大小
卷积核窗口大小
卷积核个数
BiLSTM隐藏层大小
样本训练次数		 200
100
30
20
256
[3, 4, 5]
100
256
10
Learning Rate 学习速率 0.01
Dropout 随机断开输入神经元的比例 0.50
Optimizer 优化器 Adam
Model Parameter Setting
数据集 类别 数量 总计
微博语料 正类 21 192 42 384
负类 21 192
Annotation Statistics of Micro-Blog Data
情感极性 微博文本
正类（1） 【“他们抗击疫情很成功”#纽约时报记者点赞中国战疫#】“中国抗击疫情很成功！”“不要觉得方舱舞可笑,那是医治的妙招”“隔离,是中国阻断疫情传播的诀窍”美国《纽约时报》资深健康与科技记者Donald McNeil,日前公开点赞中国“战疫”,纠正了西方媒体的歪曲和误读,认为与意大利和美国的应对形成了鲜明的对比,这段视频已经在全球已有数百万人次观看转发。
负类（0） 【#武汉肺炎疫情病原体并非SARS病毒#】连日来,武汉肺炎疫情是由新型SARS病毒引发的说法在网络流传。中国疾控中心公号1月18日发布科普文章《关于武汉病毒性肺炎,这5大谣言千万别信！》,文章提到,引起武汉病毒性肺炎疫情的病原体不是SARS病毒。目前调查显示,该病毒人际间传播能力和致病性均较SARS弱。#武汉新增4例新型冠状病毒肺炎病例#。
Sample of Micro-Blog Data
模型 P/% R/% F1/%
SVM 75.71 70.09 72.79
CNN
RNN		 80.98
78.01		 80.02
78.19		 80.50
78.10
LSTM
BiLSTM		 79.13
81.32		 79.33
80.83		 79.23
81.07
Results of Five Benchmark Model Experiments
模型 P/% R/% F1/%
CNN-G 82.04 81.41 81.72
CNN-W2V 82.16 81.73 81.94
CNN-WMF 83.77 82.90 83.33
CNN-FT 82.54 81.92 82.23
CNN-FMF 84.13 83.74 83.93
BiLSTM-G 83.26 83.00 83.13
BiLSTM-W2V 83.34 83.19 83.26
BiLSTM-WMF 84.49 84.57 84.53
BiLSTM-FT 83.67 83.79 83.73
BiLSTM-FMF 85.32 85.09 85.20
Results of Multi-Feature Fusion Experiments
模型 P/% R/% F1/%
CNN-WMF-A 85.02 84.87 84.94
CNN-FMF-A 85.34 85.03 85.18
BiLSTM-WMF-A 85.97 85.73 85.85
BiLSTM-FMF-A 86.42 86.14 86.28
Results of Attention Mechanism Experiments
模型 P/% R/% F1/%
CNN-BiLSTM-WMF-A 87.63 87.56 87.59
CNN-BiLSTM-FMF-A 88.07 88.43 88.25
MCMF-A 90.45 89.98 90.21
Results of Multi-Channel Experiments
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