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

Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA)

Topic modeling is an increasingly important component of Big Data analytics, enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM), while mathematically elegant, do not lend themselves well to direct parallelization because of dependencies from one time step to another. Data decomposition approaches that partition ...

Assignment 2: Twitter Topic Modeling with Latent Dirichlet Allocation Background

In this assignment we are going to implement a parallel MapReduce version of a popular topic modeling algorithm called Latent Dirchlet Allocation (LDA). Because it allows for exploring vast document collection, we are going to use this algorithm to see if we can automatically identify topics from a series of Tweets. For the purpose of this assignment, we are going to treat every tweet as a docu...

Topic Models - Latent Dirichlet Allocation

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 ...

Latent Dirichlet Allocation For Text And Image Topic Modeling

Latent Dirichlet allocation (LDA) is a popular unsupervised technique for topic modeling. It learns a generative model which can discover latent topics given a collection of training documents. In the unsupervised learning framework, where the class label is unavailable, it is less intuitive to evaluate the goodness-of-fit and degree of overfitting of learned model. We discuss two measurements ...

Latent Dirichlet Allocation For Text And Image Topic Modeling

Latent Dirichlet Allocation (LDA) is a generative model for text documents. It is an unsupervised method which can learn latent topics from documents. We investigate the task of topic modeling of documents using LDA, where the parameters are trained with collapsed Gibbs sampling. Since the training process is unsupervised and the true labels of the training documents are absent, it is hard to m...