I would like to know if you people have some good tutorials (fast and straightforward) about topic models and LDA, teaching intuitively how to set some parameters, what they mean and if possible, with some real examples.
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$\begingroup$ Not a direct answer and not tutorials either, but if you're interested in hierarchical models, you might find my recent answer on the topic helpful (a collection of research papers). $\endgroup$– Aleksandr BlekhJan 8, 2015 at 18:12
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2$\begingroup$ Generally questions that just ask for off-site resources are considered off topic. Maybe you can narrow down what you are looking for in a tutorial. What do you know, what do you want to know, do you want code in a particular language, etc. $\endgroup$– Sean OwenJan 8, 2015 at 23:43
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5 Answers
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If you're working in R, Carson Sievert's tutorial on using LDA to model topics in movie reviews is an excellent starting point:
http://cpsievert.github.io/LDAvis/reviews/reviews.html
This tutorial makes use of LDAvis, an interactive visualization of topic and word distributions that can really aid intuition.
Also, although not short, David M. Blei's lectures on topic models are a great resource for understanding the meaning behind the parameters: http://videolectures.net/mlss09uk_blei_tm/
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I highly recommend this tutorial: Getting Started with Topic Modeling and MALLET
Here are some additional links to help you get started...
Good introductory materials (including links to research papers): http://www.cs.princeton.edu/~blei/topicmodeling.html
Software:
- MALLET (Java): http://mallet.cs.umass.edu/topics.php
- topic modeling developer's guide: http://mallet.cs.umass.edu/topics-devel.php
- gensim (Python): http://radimrehurek.com/gensim/
- topicmodels (R): http://cran.r-project.org/web/packages/topicmodels/index.html
- Stanford Topic Modeling Toolbox (designed for use by social scientists): http://www-nlp.stanford.edu/software/tmt/tmt-0.4/
- Mr.LDA (scalable topic modeling using MapReduce): http://lintool.github.io/Mr.LDA/
- If you're working with massive amounts of input text, you might want to consider using Mr.LDA to build your topics models -- its MapReduce-based approach might be more efficient when working with lots of data.
Even more here on the Biased Estimates blog: Topic Models Reading List
answered Jan 8, 2015 at 20:58
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If you are looking for something simple to start with and easy to implement, I would recommend this.
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$\begingroup$ @pedrobisp do up vote the answer if it makes sense to you. $\endgroup$– SVKMay 16, 2017 at 21:19
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The CLARIN-D project has collected some good pointers to tutorials for topic modeling and LDA on the Teaching ans Learning Materials Collection (TeLeMaCo) site hosted by the Universität des Saarlandes CLARIN centre.
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I suggest trying Machine Learning Plu's Gensim tutorial. It will give you a holistic overview, on NLP and LDA, including: how to pre-process your data, do feature engineering and apply LDA.
