Topic Modeling of my own articles on POCKET web service.
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.
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:
You need an API account with Pocket
Gather Training and Test Data
$ python get_data.py
$ python clean_data.py
Apply LDA or LSA modeling
$ python apply_lda.py
Visualize results: lda.ipynb or lsa.ipynb