Ok, so it’s not summer any more. My defence is that I did this work during summer but I’m only writing about it now.
To recap, I’d been working on a smart filter; a system to predict articles I’d like based on articles I’d previously found interesting. I’m calling it my rss-thingy / smart filter / information assistant1. I’m tempted to call it “theia”, short for “the information assistant” and a play on “the AI”, but it sounds too much like a Siri rip-off. Which it’s not.
Aaaanyway, I’d collected 660 interesting articles and 801 that I didn’t find interesting–fewer than expected, but I had to get rid of some that were too short or weren’t articles (e.g., lists of links, or github repositories). There was also a bit of manual work to make sure none of the ‘misses’ were actually ‘hits’. I.e., I didn’t want interesting articles to turn up as misses, so I skimmed through all the misses to make sure they weren’t coincidentally interesting (there were a few). The hits and misses then went into separate folders, ready to be loaded by scikit-learn.
I used scikit-learn to vectorise the documents as a tf-idf matrix, and then trained a linear support vector machine and a naive bayes classifier. Both showed reasonable precision and recall upon my first attempt, but tests on new articles showed that the classifier tended to categorise articles as misses, even if I did find them interesting. This is not particularly surprising; most articles I’m exposed to are not particularly interesting, and such simple models trained on a relatively small dataset are unlikely to be exceptionally accurate in identifying them. I spent a little time tuning the models without getting very far and decided to take a step sideways before going further.
Eventually I’ll want to group potentially interesting articles, so I wrote up a quick topic analysis of the articles I liked, comparing non-negative matrix factorization with latent dirichlet allocation. They did a reasonable job of identifying common themes, including brain research, health research, science, technology, politics, testing, and, of course, data science.
You can see the code for this informal experiment on github.
In my next experiment (now, not SoDS18!) I plan to refine the predictions by paying more attention to cleaning and pre-processing the data. And I need to brush up on tuning these models. I’ll also use the trained models to make ranked predictions rather than simple binary classifications. The dataset will be a little bigger now at around 800 interesting articles, and a few thousand not-so-interesting.
1. Given all the trouble I have naming things, I'm really glad I haven't had to do any cache-invalidation yet.