  Topic modeling is one of the most powerful techniques in text mining for data
mining, latent data discovery, and finding relationships among data, 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 modeling, which Latent Dirichlet allocation (LDA) is one of the most
popular methods in this field. Researchers have proposed various models based
on the LDA in topic modeling. According to previous work, this paper can be
very useful and valuable for introducing LDA approaches in topic modeling. In
this paper, we investigated scholarly articles highly (between 2003 to 2016)
related to Topic Modeling based on LDA to discover the research development,
current trends and intellectual structure of topic modeling. Also, we summarize
challenges and introduce famous tools and datasets in topic modeling based on
LDA.
