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
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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