Knowledge Commons of Institute of Automation,CAS
User-concerned actionable hot topic mining: enhancing interpretability via semantic–syntactic association matrix factorization | |
Linzi Wang; Qiudan Li; Jingjun David Xu; Minjie Yuan | |
发表期刊 | Journal of Electronic Business & Digital Economics |
2022-10 | |
页码 | ISSN: 2754-4214 |
摘要 | Mining user-concerned actionable and interpretable hot topics will help management departments fully grasp the latest events and make timely decisions. Existing topic models primarily integrate word embedding and matrix decomposition, which only generates keyword-based hot topics with weak interpretability, making it difficult to meet the specific needs of users. Mining phrase-based hot topics with syntactic dependency structure have been proven to model structure information effectively. A key challenge lies in the effective integration of the above information into the hot topic mining process. |
七大方向——子方向分类 | 社会计算 |
国重实验室规划方向分类 | 社会系统建模与计算 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51856 |
专题 | 舆论大数据科学与技术应用联合实验室 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Qiudan Li |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences 3.Department of Information Systems, City University of Hong Kong 4.Institute of Automation, Chinese Academy of Sciences; School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Linzi Wang,Qiudan Li,Jingjun David Xu,等. User-concerned actionable hot topic mining: enhancing interpretability via semantic–syntactic association matrix factorization[J]. Journal of Electronic Business & Digital Economics,2022:ISSN: 2754-4214. |
APA | Linzi Wang,Qiudan Li,Jingjun David Xu,&Minjie Yuan.(2022).User-concerned actionable hot topic mining: enhancing interpretability via semantic–syntactic association matrix factorization.Journal of Electronic Business & Digital Economics,ISSN: 2754-4214. |
MLA | Linzi Wang,et al."User-concerned actionable hot topic mining: enhancing interpretability via semantic–syntactic association matrix factorization".Journal of Electronic Business & Digital Economics (2022):ISSN: 2754-4214. |
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User-concerned actio(2079KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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