I’ve posted three examples of Twitter hashtags datasets in the last week: one on China, one on Iran, and one on Algeria. In order to build these datasets, I needed to obtain older tweets; this is slightly more difficult than simply filtering the streaming feed for your hashtag of choice. The original code I wrote
Earlier this week, I posted a ~100k tweet dataset on the #25bahman protests in Iran. The corresponding figure of frequencies showed a strong presence on Twitter, with over 500 tweets per 5 minute period at peak. You can download the dataset or check out the figure in that post. I decided to take a quick
My first experience with R was in 2007 as a sophomore in undergrad. As part of a larger project on pricing day-ahead electricity futures, I wanted to cluster locational marginal price (LMP) data from the ISO-NE. Something like k-means is easy to plot and visualize in low-dimensions, but this data was better approached by hierarchical methods.
You may have heard about the recently leaked presentation on combating Wikileaks that was produced by employees of HBGary Federal, Palantir Tech, and Berico Tech. You may have also heard that Anonymous retaliated against HB Gary Federal for threatening to release their identities. I thought it would be interesting to run some analysis of the email networks
Let's say that you want to take a set of documents and apply a computational linguistic technique. If your method is based on the bag-of-words model, you probably need to pre-process these documents first by segmenting, tokenizing, stripping, stopwording, and stemming each one (phew, that's a lot of -ing's). In the past, I've relied
