I haven’t done any painting in a while, and frankly, I missed it a little.  So a couple weeks ago, when I visited NYC with Natalie, I saw a painting at the Metropolitan Museum of Art that I just fell in love with. Read more…

Are you tired of micro-managing your likes?  We all know that a well-placed like on Facebook can be hilarious. But clicking like over and over is so difficult! Now you can just like everything with one easy click… Read more…

I was sitting in my networks class today, thinking of how it would be possible to implement an algorithm for taking into consideration the similarity of documents for teasing apart temporal interference, when I started coming to a more coherent model of what I’ve been trying to do in general.  This article will set up some early ideas for a model of what’s going on, what we’re attempting to accomplish, and possible general procedures for doing so.  It also sets up some terminology. Read more…

Last time, I generated a few data sets for testing different methods of rating the training news articles.  This time, I actually implemented two of them, the naive approach I had used before, and the new-and-improved version taking into account temporal interference. Read more…

From my last post, I introduced the idea of creating test data sets for the purpose of finding an algorithm to tease apart the influence of individual news articles.  I have done just that and am posting the data sets for further analysis. Read more…

This article is the second of the Automated News Analysis series, regarding a particular problem I overlooked during my first try at news analysis.  The subject of this article is taking a dataset of news and price history, and attempting to assign sentiment to the news articles for which we know the price development.  The problem that we will explore in particular, is removing influences from other news articles near our target article in time. Read more…