Topic Modeling

Neural Topic Models you own this product

This project is part of the liveProject series Traditional and Neural Topic Modeling
prerequisites
intermediate Python • linear algebra • basics of deep learning
skills learned
exploring the vector representations produced by BERT • preprocessing text documents • using the Contextual Topic Model • performing hyperparameter tuning • evaluating topics using Coherence and Diversity and visualizing generated topics
Aneesha Bakharia
1 week · 6-8 hours per week · INTERMEDIATE

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Look inside

In this liveProject, you’ll use the neural network-inspired Contextual Topic Model to identify and visualize all of the articles in a scientific magazine’s back catalog. This cutting-edge technique is made easy by the OCTIS (Optimizing and Comparing Topic Models is Simple!) library. Once you’ve established your text-processing pipeline, you’ll use coherence and diversity metrics to evaluate the output of your topic models, tune your neural network’s hyperparameters to improve results, and visualize your results for printing on posters and other media.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Aneesha Bakharia
Aneesha Bakharia completed her PhD in interactive topic modeling at Queensland University of Technology in Australia. She is currently the Manager of Learning Analytics at The University of Queensland where she leads a team of programmers and data scientists. She has written 10 books on programming and web development for Cengage publishing. She also blogs about data science related topics, including topic modeling and she publishes academic peer reviewed publications on educational technologies and learning analytics.

prerequisites

This liveProject is for data scientists and developers who are confident programming with Python and the Python data ecosystem. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of Jupyter Notebook
TECHNIQUES
  • Linear algebra
  • Basics of deep learning

you will learn

In this liveProject, you’ll master topic modeling—an amazing skill for quickly analyzing textual datasets. You’ll learn to apply the cutting-edge neural topic model in a real-world setting:


  • Exploring the vector representations produced by the Bidirectional Encoder Representations from Transformers (BERT) neural network and their use in the Contextual Topic Model (CTM)
  • Using the Contextual Topic Model and comparing to the output of Latent Dirichlet Allocation
  • Preprocess text documents
  • Using Topic Coherence and Diversity metrics
  • Visualizing derived topics with a variety of techniques

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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