Dear everyone, I am new to R, and I am looking at doing text classification on a huge collection of documents (>500,000) which are distributed among 300 classes (so basically, this is my training data). Would someone please be kind enough to let me know about the R packages to use and their scalability (time and space)?
I am very new to R and do not know of the right packages to use. I started off by trying to use the tm package (http://cran.r-project.org/package=tm) for pre-processing and FSelector (http://cran.r-project.org/web/packages/FSelector/index.html) package for feature selection - but both of these are incredibly slow and completely unusable for my task. So the question is what are the right packages to use (for pre-processing, feature selection, and classification)? Please consider the fact that I may be dealing with data of millions of dimensions which may not even fit in memory. I posted on this issue twice (http://r.789695.n4.nabble.com/Entropy-based-feature-selection-in-R-td3708056.html , http://r.789695.n4.nabble.com/R-s-handling-of-high-dimensional-data-td3741758.html) but did not get any response. This is a very critical piece of my research and I have been struggling with this issue for a long time. Please consider helping me out, directly or by pointing me to any other software/website that you think may be more appropriate. Many thanks in advance. -- View this message in context: http://r.789695.n4.nabble.com/Classifying-large-text-corpora-using-R-tp3786787p3786787.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
