Semantic Scholar Open Access 2017 1669 sitasi

Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Hamed Jelodar Yongli Wang Chi Yuan Xia Feng

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Hamed Jelodar

Yongli Wang

Chi Yuan

Xia Feng

Format Sitasi

APA MLA BibTeX

Jelodar, H., Wang, Y., Yuan, C., Feng, X. (2017). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. https://doi.org/10.1007/s11042-018-6894-4

Akses Cepat

Lihat di Sumber doi.org/10.1007/s11042-018-6894-4

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Total Sitasi: 1669×
Sumber Database: Semantic Scholar
DOI: 10.1007/s11042-018-6894-4
Akses: Open Access ✓