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I am new to Machine Learning.I am working on a project where the machine learning concept need to be applied.

Problem Statement:

I have large number(say 3000)key words.These need to be classified into seven fixed categories.Each category is having training data(sample keywords).I need to come with a algorithm, when a new keyword is passed to that,it should predict to which category this key word belongs to.

I am not aware of which text classification technique need to applied for this.do we have any tools that can be used.

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  • $\begingroup$ Linear SVM works very well for text classification. $\endgroup$ Nov 22, 2014 at 18:20
  • $\begingroup$ Thanks for your response.Do we have any tools that works best for this technique. $\endgroup$
    – harish
    Nov 22, 2014 at 18:43
  • $\begingroup$ can SVM be used for classifying text into seven different categories. $\endgroup$
    – harish
    Nov 22, 2014 at 18:55
  • $\begingroup$ Is there an example for this problem online? A basic implementation with examples for the specified problem? $\endgroup$
    – Tobias
    Aug 20, 2015 at 11:50

1 Answer 1

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Linear SVM works well for text classification (and is very fast). The reason linear methods work well is because text classification typically has a high dimensional input space.

SVM classifiers are implicitly binary. You can do multiclass classification by making several classifiers, the most common approach being one-vs-all (e.g. pick one class as positive and all others as negative and do this 7 times). Most implementations provide multiclass formulations so you don't have to worry about what it does under the hood.

You can find implementations in many different packages. Not sure what software you are using at the moment, but linear SVM is available in many environments:

You can probably find an SVM implementation in any language.

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  • $\begingroup$ I am planning to implement it using WEKA Tool $\endgroup$
    – harish
    Nov 22, 2014 at 18:56
  • $\begingroup$ @harish WEKA has linear SVM, cfr. weka.sourceforge.net/doc.stable/weka/classifiers/functions/… $\endgroup$ Nov 22, 2014 at 18:57
  • $\begingroup$ Thank you so much for your quick response.I would like to brief my problem statement.It's basically key words(bigrams/unigrams) .For example the keywords(file management,performance,process distribution etc) should be mapped to a category named "Computer Infrastructure.what would be the step by step procedure for this.Do I need to come with a dictionary of these terms,as these are not a plain text words,where I can use existing dictionaries $\endgroup$
    – harish
    Nov 22, 2014 at 19:01

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