Hi, I am implementing slightly different variation of this solution. I need some guidance.
I have a CSV file with two columns, REMARKS and CATEGORY. Based on the remarks, I train naïve bayes model which would automatically assign categories to REMARKS. I followed this link http://chimpler.wordpress.com/2013/03/13/using-the-mahout-naive-bayes-classifier-to-automatically-classify-twitter-messages/ It works fine. Now I have a slightly different requirement. I want the text in REMARKS column to be tokenized in a different fashion. I have some keywords. When those keywords occur in REMARKS text, I want them to be intact and splitted further. For example, if REMARKS text is "Sump pressure is low", with default analyzer, it would be split into four tokens as "Sump", "pressure", "is", "low". But I want it to be tokenized as "Sump pressure", "is", "low". I have implemented a custom tokenizer which would do this. Now I want to vectorize this. I tried the pseudo code suggested below. I don't know how to serialize these vectors into sequence files. When I run seq2sparse, apart from vectors, it creates some other labelindex and dictionary files. I could not see the code to create those files here. Am I missing something? I have started looking into org.apache.mahout.vectorizer package. Any pointers would be of great help. Regards, Anand.C -----Original Message----- From: Suneel Marthi [mailto:[email protected]] Sent: Tuesday, May 21, 2013 10:21 PM To: [email protected] 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 <[email protected]> To: "[email protected]" <[email protected]> Sent: Tuesday, May 21, 2013 12:20 PM Subject: Re: Feature vector generation from Bag-of-Words 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 <[email protected]> To: "[email protected]" <[email protected]> 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 ::DISCLAIMER:: ---------------------------------------------------------------------------------------------------------------------------------------------------- The contents of this e-mail and any attachment(s) are confidential and intended for the named recipient(s) only. 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