1. Topic Modeling using Latent Dirichlet Allocation

2. Topic Modeling
A process of analyzing large collections of documents in order to discover latent topics from the documents.
Able to organize and structure the documents
Discover the different topics that a documents has
How similar are certain documents

4. Latent Dirichlet Allocation (LDA)
It is a unsupervised learning
Produces a generative model

5. Terminology Word: w ∈ {1,…,V} Document: Sequence of N words
Corpus: which is a set of M documents
Topic: z ∈ {1,…, K}

6. Topic
A topic is a set of co-occurring terms

7. Generate Process Choose N based on Poisson distribution
Choose θ based on Dirichlet distribution (θ is a topic weight vector)
For each of the N words:
Choose z from θ
Choose w from z

8. Learning
Variational Bayes
Gibbs Sampling

9. Applications of LDA
Collaborative Filtering
Spam Detection
Music
Image

10. References
D M Blei, A Y Ng, M I Jordan. (2003). Latent Dirichlet Allocation. The Journal of Machine Learning Research
D J Hu. (2009). Latent Dirichlet Allocation for text, images, and music.