GPLDA: A Generalized Poisson Latent Dirichlet Topic Model

Topic Models - Latent Dirichlet Allocation

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...

Decentralized Topic Modelling with Latent Dirichlet Allocation

Privacy preserving networks can be modelled as decentralized networks (e.g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by a master or central node. For this type of networks, the main issue is to gather/learn global information on the network (e.g., by optimizing a global cost function) while keeping the (sensitive) information ...

Topic Trend Detection in Text Collections using Latent Dirichlet Allocation

Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in many domains. Traditionally, the task of topic discovery has been mainly addressed through algorithms that work on a snapshot view of the documents, which ignores the ...

Topic Classification using Latent Dirichlet Allocation at Multiple Levels

We propose a novel low-dimensional text representation method for topic classification. Several Latent Dirichet Allocation (LDA) models are built on a large amount of unlabelled data, in order to extract potential topic clusters, at different levels of generalization. Each document is represented as a distribution over these topic clusters. We experiment with two datasets. We collected the firs...