Stuti,

Here's how I would do it.

1.  Create a collection of the 100 keywords that r of interest.

     Collection<String> keywords = new ArrayList<String>();
     keywords.addAll(<your 100 keywords>);
     

2.  For each word in each of the text documents create a Multiset (which is a 
bag of words) ,
      retain only those terms of interest from (1) that are of interest and use 
Mahout's StaticWordValu

     // Itertate through all the documents
     for document in documents {

      //create a bag of words for each document
       Multiset<String> multiset = new HashMultiset<String>();

     // create a RandomAccessSparseVector
     Vector v = new RandomAccessSparseVector(100); // 100 features for the 100 
keywords 

        for term in document.terms {
            multiset.add(term);
        }

        // retain only those keywords that are of interest (from step 1)
        multiset.retainAll(keywords);

       // You now have a bag of words containing only the keywords with their 
term frequencies
      
      // Use one of the Feature Encoders, refer to Section 14.3 of Mahout in 
Action for more detailed description of
      // this process

       FeatureVectorEncoder encoder = new StaticWordValueEncoder("body");
      
     for (Multiset.Entry<String> entry : multiset.entrySet()) {
       encoder.addToVector(entry.getElement(), entry.getCount(), v);
     }



     


     




________________________________
 From: Stuti Awasthi <stutiawas...@hcl.com>
To: "user@mahout.apache.org" <user@mahout.apache.org> 
Sent: Tuesday, May 21, 2013 7:17 AM
Subject: Feature vector generation from Bag-of-Words
 

Hi all,

I have a query regarding the Feature Vector generation for Text documents.
I have read Mahout in Action and understood how to create the text document in 
feature vector weighed by Tf of Tfidf schemes. My usecase is a little tweaked 
with that.

I have few keywords may be say 100 and I want to create the Feature Vector of 
the text documents only with these 100 keywords. So I would like to calculate 
the frequency of each keyword in each document and generate the feature vector 
of the keyword with the frequency as weights.

Is there any already present way to do this or Il need to write the custom code?

Thanks
Stuti Awasthi


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