Tagged ‘automated news analysis’

Last time, I tried out a couple different methods to assign sentiment to news articles, and found that the best performance seemed to come from using my Temporal Interference method initialized by zeroes.  Well there’s a little more information available to us, and that’s the news article content themselves! Read more…

Last time I introduced some test data, and before that I formalized the Perturbation Model for Price Moves a bit further.  Well this required me to rewrite the code I had written before for Sentiment analysis.  I took advantage of interval trees to make my code fairly efficient, and also changed the way I initialize the price movements, yielding minor improvements over the naive methods. Read more…

I got back to working on the automated news analysis algorithm again, and thought that it would be wise to generate some new test data that will have some more context to it.  I wrote a simple algorithm that I discuss here, and I generated some data sets. 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…