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

By Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng. Multimedia tools and applications, 2017

1,778 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b22de434b462558a127f327f29e2b0c673c0d7ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Apurva Shah
2. Hamed Jelodar
3. Yongli Wang
4. Uttam Chauhan

Citations in the corpus

• Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach (2020, methodology): “formerly unseen documents, [15]–[17].”
• Topic Modeling Using Latent Dirichlet allocation (2021, background): “The authors reviewed the most significant articles associated with LDA [55].”