Thanks for your response! I've been researching ways of implementing feature selection approaches (using information gain) within R and there seem to be some packages available to do this (e.g. ) but I'm unclear as to how to integrate this with RTextTools.
Hi Alexandre, thanks for this very helpful SVM tutorial! I've been doing some other research on using SVM for text classification and I'm unable to figure out a way to extract the key features (e.g. words) that are most representative of each category? So, for instance, if i have text which is comprised of fruits and vegetables (e.g. tomato, avocado, banana, apple, celery, potato, etc.), is there a way to see which words fall into which category? Thanks!
My R interface has been pretty basic in the last few years. I have usually stuck to the R console. Yes, I’ve tried with ; a staple, but it is so unbearably antiquated that I always gave up on its significant learning curve. GUI packages–especially –offer viable alternatives, but I feel the GUI lets me lose focus of the code. I have been envious of TextMate for Mac, but alas, I’m not a Mac user. Recently, though, I’ve moved to . With some nudging, I have been able to mimic the typical R console environment in the more-powerful Sublime Text program.
thank you for your tutorial. I have trained the SVM model and now I would like to save it in PMML format to call the model in Java-code. I have experience of saving SVM models from e1071 pacckage but it doesnt work for the model from RTextTools package.