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From Suneel Marthi <>
Subject Re: Feature vector generation from Bag-of-Words
Date Wed, 22 May 2013 12:43:21 GMT
See inline.

 From: Stuti Awasthi <>
To: "''" <> 
Sent: Wednesday, May 22, 2013 7:02 AM
Subject: RE: Feature vector generation from Bag-of-Words

Hi Suneel,

I implemented your suggested approach. This was simple to implement and you have made the
steps pretty clear. Thankyou :) . I have few query in creating Features using Multiset:

1. Can't we consider keyword Case Insensitiveness using multiset i.e my keyword may be "Day"
and in document it may be "day". 

>>Yes, you can if that's a requirement for you. Convert all keywords to lowercase before
storing them in multiset.

2. Can we use the  multiset to contain the words which might match the keyword regex rather
than exact keyword i.e. if keyword is "Recommend" and in the document it is "Recommended"
then it should take care of it.

>> What you are describing is called 'Stemming'.  Lucene should be able to help you

Any pointers ?

Stuti Awasthi

-----Original Message-----
From: Stuti Awasthi 
Sent: Wednesday, May 22, 2013 12:01 PM
Subject: RE: Feature vector generation from Bag-of-Words

Thanks Suneel,

I will go through your approach and will also learn more about various api's you have suggested.
I am new to Mahout so will need to dig more. :)

By the time I was thinking the approach like this :
1. Create the sequence file of Bad of words and Input Data in different documents 2. For individual
documents , Il loop through 100 keywords and count the number of time each keyword occur in
a document 3.Create the RandomAccessSparseVector to store keyword and its frequency for each

This is not the good approach to do may be due to Step 2 , but this approach can also be implemented
using MR. Please provide your thoughts on this.


-----Original Message-----
From: Suneel Marthi []
Sent: Tuesday, May 21, 2013 10:21 PM
Subject: Re: Feature vector generation from Bag-of-Words

It should be easy to convert the below pseudocode to MapReduce to scale for large collection
of documents.

From: Suneel Marthi <>
To: "" <>
Sent: Tuesday, May 21, 2013 12:20 PM
Subject: Re: Feature vector generation from Bag-of-Words


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

     // 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 {

        // retain only those keywords that are of interest (from step 1)

       // 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 <>
To: "" <> 
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?

Stuti Awasthi


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