Topic Modeling of my own articles on POCKET web service.

Summary:

This project is derived from my over-zealous saving of web articles. The reason that I save articles is to learn about a topic. I hoped by doing this project; I could cluster my articles into bite-size chunks that would allow for easier learning.

Results:

Using Latent Dirichlet(LDA) and Latent Semantic Analysis(LSA) I performed topic modeling on 1000 articles that I saved over the past three years into my Pocket account.

With LDA modeling, I found that the central topics were:

With LSA modeling, I found that the central topics were:

Run your own analysis: ---------------------1. You need an API account with Pocket 2. Gather Training and Test Data :: $ python get_data.py 3. Clean Data :: $ python clean_data.py 4. Apply LDA or LSA modeling :: $ python apply_lda.py 5. Visualize results: lda.ipynb or lsa.ipynb