Topic Modeling of Online Media News Titles during COVID-19 Emergency Response in Indonesia Using the Latent Dirichlet Allocation (LDA) Algorithm

Automatic keyword extraction using Latent Dirichlet Allocation topic modeling: Similarity with golden standard and users' evaluation

Purpose: This study investigates the automatic keyword extraction from the table of contents of Persian e-books in the field of science using LDA topic modeling, evaluating their similarity with golden standard, and users' viewpoints of the model keywords. Methodology: This is a mixed text-mining research in which LDA topic modeling is used to extract keywords from the table of contents of sci...

Topic Modeling For Associated Press Articles Using Latent Dirichlet Allocation [LDA]

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey

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, ...

TM-LDA: Efficient Online Modeling of the Latent Topic Transitions in Social Media

Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments s...

Topic Models - Latent Dirichlet Allocation