With RTextTools now released and the feedback rolling in, the development team is getting the ball rolling on the help documentation for the library. Currently, you cannot access help files about the library or its functions from within R. However, we do offer a draft of a quick start guide in PDF format under the Documentation section of the website.

Stay tuned for a release in the next few months with a slew of new features and refinements.

The biggest complaint we had during the installation process was that Xcode (account required) and Rtools were required for MacOS X and Windows. Today we released universal binaries (PPC/i386/x86_64) for MacOS 10.5+ as well as binaries (i386/x86_64) for Windows. This addition will significantly reduce the amount of time it takes to install RTextTools.

We're gearing up to unveil RTextTools next Friday in Catania. See you there!

When we first wrote RTextTools, we opted to use RWeka for boosting and bagging algorithms for lack of a better alternative. We've discovered that this leads to all sorts of ugly rJava installation issues across platforms and prevents our users from getting started quickly. Recently, we've stumbled upon two excellent non-Java alternatives: LogitBoost in the caTools package, and the bagging implementation in the ipred package.

Consequently, we've eliminated RWeka from the list of dependencies and significantly streamlined the installation process. Windows users only need to deal with the Rtools installation now; developer Loren Collingwood left a helpful comment regarding Rtools in a previous post.

After several weeks trying to find the source of a bug in the maximum entropy library when compiling on Windows, Dirk Eddelbuttel pointed me in the right direction to resolve the issue. Although it required a re-write of the library using the new Rcpp API, maximum entropy now installs on Windows machines when Rtools is installed.

This is significant because now RTextTools has two low-memory algorithms (support vector machines and maximum entropy) working cross-platform. This significantly raises accuracy for Windows users, and simplifies the installation process.

In preparation for The 4th Annual Conference of the Comparative Policy Agendas Project in Catania, Sicily, our development team has been busy drafting the documentation for RTextTools. In addition to standard documentation of functions, we want to provide quick-start guides, sample datasets, example scripts, and Amazon EC2 instructions to make it as easy as possible for researchers to get up and running.

A big part of developing the documentation is understanding what methods work best for which datasets. We welcome any feedback you may have regarding what worked for you, and what dataset you were operating on. You can find our contact information on the About the Project page.

One key limiting factor for automated text classification is memory consumption. As you accumulate more news articles, bills, and legal opinions, the term-document matrices used to represent the data grow quickly. RTextTools provides two algorithms, support vector machines and maximum entropy, that can handle large datasets with very little memory. Luckily, these two algorithms tend to be the most accurate as well. However, some applications require an ensemble of more than two algorithms to get an accurate scoring of topic codes.

First, you can try reducing the number of terms in your matrix. The create_matrix() function provides many features that can help remove noise from your dataset. There are the defaults- removing stopwords, removing punctuation, making words lowercase, and stripping whitespace- but also some other helpful tools. You can set minimum word length (e.g. minWordLength=5), select the N most frequent terms from each document (e.g. selectFreqTerms=25), setting a minimum word frequency per document (e.g. minDocFreq=3), and remove terms with a sparse factor of less than N (e.g. removeSparseTerms=0.9998).

These options can help you reduce the size of your document matrix, but they also can remove some information that may be valuable for the learning algorithms. If you just need the resources to run a huge dataset, and nothing above helps, you should look into setting up an Amazon EC2 instance with RStudio installed. We plan to create a simple way of doing this in the near future, but you'll have to brave the stormy waters for now. Be warned, this option is for experienced users only!