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

Hamed Jelodar; Yongli Wang; Chi Yuan; Xia Feng; Xiahui Jiang; Yanchao Li; Liang Zhao

doi:10.1007/s11042-018-6894-4

Abstract

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.

Keywords

Latent Dirichlet allocationTopic modelComputer scienceData scienceField (mathematics)Artificial intelligenceInformation retrieval

Affiliated Institutions

Nanjing University of Science and Technology CN

Related Publications

A correlated topic model of Science

David M. Blei , John Lafferty

Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes ...

2018 OPAL (Open@LaTrobe) (La Trobe Univers... 1137 citations

Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

Junbo Yi , Tetsuya Nasukawa , Răzvan Bunescu +1 more

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document ab...

2004 719 citations

Deep Learning--based Text Classification

Shervin Minaee , Nal Kalchbrenner , Erik Cambria +3 more

Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, ...

2021 ACM Computing Surveys 1298 citations

Deep Contextualized Word Representations

Matthew E. Peters , Mark E Neumann , Mohit Iyyer +4 more

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses ...

2018 Proceedings of the 2018 Conference of... 1786 citations

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Pengfei Liu , Weizhe Yuan , Jinlan Fu +3 more

This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning,...

2022 ACM Computing Surveys 3108 citations

Publication Info

Year: 2018
Type: article
Volume: 78
Issue: 11
Pages: 15169-15211
Citations: 1659
Access: Closed

External Links

Download PDF (Free) View on DOI.org arXiv Semantic Scholar

Social Impact

Altmetric

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

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

1659

OpenAlex

Influential

Cite This

APA Style

                            
                                
                                    Hamed Jelodar, 
                                
                                    Yongli Wang, 
                                
                                    Chi Yuan
                                
                                et al.
                            
                            (2018). 
                            Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. 
                            Multimedia Tools and Applications
                            , 78
                            (11)
                            , 15169-15211.
                            https://doi.org/10.1007/s11042-018-6894-4
                        

Identifiers

DOI: 10.1007/s11042-018-6894-4
arXiv: 1711.04305

Data Quality

Data completeness: 84%