Text Classification in R [closed]

Question

New to R, and am trying to do text classification. I am using R package tm to convert raw txt data into matrix. Here's the relevant code snippet.

 col <- Corpus(DirSource(path),
                readerControl = list(reader = readPlain,
                language = "en",
                load = TRUE)))

 tdm <-  TermDocumentMatrix(col, control = list(tokenize = NGramTokenizer))

I have the following questions:

1) Feature selection

I need to do chi-squared or information gain based feature selection on my data. Which R packages can I look at? I came across at caret and boruta but they do not seem to be appropriate for what I am wanting to do.

2) Handling new (unseen) instances

Let's say I have trained my model using my training set. When the test set comes in, I would need to pass it through same filters (stemming, stopword removal, tf-idf weighting, feature selection etc.). I have no idea how to do this !

Any hint/help will be much appreciated.

Thanks in advance.

Answer 1

Regarding feature selection, as you described, depends on the text content ! How do you classify them manually ?! Because of some impression or words ?!(negative - positive) (happy - sad - neutral). Frequency of words could be also informative. check out this package.

There are several review and papers like this on net.

You can define your own kernel check kernel methods, in order to put similar text together and distinguish dissimilar. check string kernels from kernlab and here

For implementation for text classification in R, look at this list and find your interest method and package.

To address your second question, I can say, do everything(Feature extraction - and all other you mention) + label of the class. Then choose part of data, say 70% and make your own model. Afterward, run your model on the unseen data(30%) and check its output with actual label. then you simply can calculate the accuracy of model.