Document Clustering with Python
In this guide, I will explain how to cluster a set of documents using Python. My motivating example is to identify the latent structures within the synopses of the top 100 films of all time (per an IMDB list). See the original postfor a more detailed discussion on the example. This guide covers:
- tokenizing and stemming each synopsis
- transforming the corpus into vector space using tf-idf
- calculating cosine distance between each document as a measure of similarity
- clustering the documents using the k-means algorithm
- using multidimensional scaling to reduce dimensionality within the corpus
- plotting the clustering output using matplotlib and mpld3
- conducting a hierarchical clustering on the corpus using Ward clustering
- plotting a Ward dendrogram
- topic modeling using Latent Dirichlet Allocation (LDA)
The 'cluster_analysis' workbook is fully functional; the 'cluster_analysis_web' workbook has been trimmed down for the purpose of creating this walkthrough. Feel free to download the repo and use 'cluster_analysis' to step through the guide yourself.
How the repo is set up
Once you've pulled down the repo, all you need to do is run 'cluster_analysis.ipynb'; it will find the various lists of synopses and titles. The 'Film_Scrape.ipynb' contains the code I used to actually scrape the synopses, in case you are interested. The other items in the repo are mostly incidentals for setting up the webpage walk-through. There is also one pickled model.
At some point in the future I'll write up how I executed the web scraping in case it's of interest.