Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture
This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie stre...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6630811 |
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| _version_ | 1832550551785570304 |
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| author | Qi Zhang Zufan Zhang Maobin Yang Lianxiang Zhu |
| author_facet | Qi Zhang Zufan Zhang Maobin Yang Lianxiang Zhu |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods. |
| format | Article |
| id | doaj-art-53f89bab442b4af8b6e11301ac803625 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-53f89bab442b4af8b6e11301ac8036252025-02-03T06:06:30ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66308116630811Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning ArchitectureQi Zhang0Zufan Zhang1Maobin Yang2Lianxiang Zhu3School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computer Science, Xi’an Shiyou University, Xi’an 710065, ChinaThis paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods.http://dx.doi.org/10.1155/2021/6630811 |
| spellingShingle | Qi Zhang Zufan Zhang Maobin Yang Lianxiang Zhu Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture Complexity |
| title | Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture |
| title_full | Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture |
| title_fullStr | Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture |
| title_full_unstemmed | Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture |
| title_short | Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture |
| title_sort | exploring coevolution of emotional contagion and behavior for microblog sentiment analysis a deep learning architecture |
| url | http://dx.doi.org/10.1155/2021/6630811 |
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