Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
复制标题
DOI：
10.1007/s11042-018-6894-4
复制
发表时间：
2019-06-01
影响因子：
3.6
通讯作者：
Zhao, Liang
中科院分区：
文献类型：
--
作者：
Jelodar, Hamed;Wang, Yongli;Zhao, Liang
主题建模是文本挖掘中最强大的技术之一，用于数据挖掘、潜在数据发现以及发现数据和文本文档之间的关系。研究人员在主题建模领域发表了许多文章，并应用于软件工程、政治学、医学和语言学等各个领域。主题建模的方法多种多样，潜在狄利克雷分配（LDA）是该领域最流行的方法之一。在主题建模中，研究者们提出了各种基于LDA的模型。根据以往的工作，本文将是非常有用的和有价值的LDA方法引入主题建模。本文通过对2003年至2016年期间与基于LDA的主题建模相关的高学术性文章进行调查，发现主题建模的研究进展，当前趋势和知识结构。此外，我们总结了挑战，并介绍了著名的工具和数据集的主题建模基于LDA。
Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.