User-concerned actionable hot topic mining: enhancing interpretability via semantic–syntactic association matrix factorization
文献类型:期刊论文
作者 | Linzi Wang; Qiudan Li; Jingjun David Xu; Minjie Yuan |
刊名 | Journal of Electronic Business & Digital Economics |
出版日期 | 2022-10 |
页码 | ISSN: 2754-4214 |
英文摘要 | 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. |
源URL | [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 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
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,et al. 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. |
入库方式: OAI收割
来源:自动化研究所
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