Leveraging multidimensional features for policy opinion sentiment prediction | |
Hou, Wenju; Li, Ying; Liu, Yijun1,2![]() ![]() | |
Source Publication | INFORMATION SCIENCES
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Keyword | Policy opinion Sentiment prediction Deep learning Feature engineering |
Abstract | Previous online policy opinion analyses based on social media data have focused on topic detection and sentiment classification of policy opinion after a given period following pol-icy implementation. These approaches are limited and inefficient because they provide no opportunity to change citizens' opinions once they have been formed. Furthermore, incor-porating auxiliary information to enrich semantic representations is vital and challenging due to limited texts, and a lack of both semantic information and strict syntactic structure. Therefore, we propose a novel framework to extract and integrate multidimensional fea-tures from user-related and policy-related social media information and predict policy comment polarity in the policy release phase. First, we construct four machine learning models for model-induced features to capture topic-related and opinion-related features and identify the policy-opinion nexus. In addition, we integrate basic and behavioral user features. Then, we leverage multidimensional features to construct a stacked learning model for predicting the policy opinion. Finally, we conduct experiments on 20 policy com-ment datasets to demonstrate that our prediction framework can effectively predict public opinion about a policy once it is released. Our model provides key insights into policy opin-ions in advance and can enable policymakers to engage in better policy communication before opinion formation. (c) 2022 Elsevier Inc. All rights reserved. |
2022 | |
Volume | 610Pages:215 |
ISSN | 0020-0255 |
Subtype | Article |
DOI | 10.1016/j.ins.2022.08.004 |
WOS Keyword | SOCIAL MEDIA ; TWITTER ; NETWORK ; USERS |
Language | 英语 |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Information Systems |
WOS ID | WOS:000848341500014 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.casisd.cn/handle/190111/12046 |
Collection | 系统分析与管理研究所 |
Affiliation | 1.Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun, Peoples R China 2.Chinese Acad Sci, Inst Sci & Dev, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing, Peoples R China |
Recommended Citation GB/T 7714 | Hou, Wenju,Li, Ying,Liu, Yijun,et al. Leveraging multidimensional features for policy opinion sentiment prediction[J]. INFORMATION SCIENCES,2022,610:215. |
APA | Hou, Wenju,Li, Ying,Liu, Yijun,&Li, Qianqian.(2022).Leveraging multidimensional features for policy opinion sentiment prediction.INFORMATION SCIENCES,610,215. |
MLA | Hou, Wenju,et al."Leveraging multidimensional features for policy opinion sentiment prediction".INFORMATION SCIENCES 610(2022):215. |
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