atmodel_plot

New Gensim feature: Author-topic modeling. LDA with metadata.

Ólavur Mortensen gensim

The author-topic model is an extension of Latent Dirichlet Allocation that allows data scientists to build topic representations of attached author labels. These author labels can represent any kind of discrete metadata attached to documents, for example, tags on posts on the web. In December of 2016, I wrote a blog post explaining that a Gensim implementation was on its …

20156116-data-concept-computer-keyboard-with-word-data-processing-selected-focus-on-enter-button-background-3-stock-photo

Topic Modelling with Latent Dirichlet Allocation: How to pre-process data and tune your model. New tutorial.

Ólavur Mortensen gensim, Machine Learning, Open Source, programming, Student Incubator

If you’ve learned how to train topic models in Gensim, but aren’t able to get satisfying results, then we have a new tutorial that will help you get on the right track on GitHub. Primarily, you will learn some things about pre-processing text data for the LDA model. You will also get some tips about how to set the parameters …

Author Topic Model

Author-topic models: why I am working on a new implementation

Ólavur Mortensen gensim, Machine Learning, Open Source, programming, Student Incubator

Author-topic models promise to give data scientists a tool to simultaneously gain insight about authorship and content in terms of latent topics. The model is closely related to Latent Dirichlet Allocation (LDA). Basically, each author can be associated with multiple documents, and each document can be attributed to multiple authors. The model learns topic representations for each author, so that …