Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 78, Issue 11, Pages 15169-15211Publisher
SPRINGER
DOI: 10.1007/s11042-018-6894-4
Keywords
Topic modeling; Latent Dirichlet allocation; Tag recommendation; Semantic web; Gibbs sampling
Categories
Funding
- National Natural Science Foundation of China [61170035, 61272420, 81674099, 61502233]
- Fundamental Research Fund for the Central Universities [30916011328, 30918015103]
- Nanjing Science and Technology Development Plan Project [201805036]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available