I have set of documents and I want classify them to true and false

My question is I have to take the whole words in the documents then I classify them depend on the similarity words in these documents or I can take only some words that I interested in then I compare it with the documents. Which one is more efficient in classify document and can work with SVM.

  • $\begingroup$ Hi @Ali, it would help if you added some more detail. What do you mean by "similarity words"? are you talking about some preprocessing step? SVMs don't use a similarity measure. $\endgroup$
    – Sean Owen
    Aug 7, 2014 at 23:39

1 Answer 1


Both methods work. However, if you retain all words in documents you would essentially be working with high dimensional vectors (each term representing one dimension). Consequently, a classifier, e.g. SVM, would take more time to converge.

It is thus a standard practice to reduce the term-space dimensionality by pre-processing steps such as stop-word removal, stemming, Principal Component Analysis (PCA) etc.

One approach could be to analyze the document corpora by a topic modelling technique such as LDA and then retaining only those words which are representative of the topics, i.e. those which have high membership values in a single topic class.

Another approach (inspired by information retrieval) could be to retain the top K tf-idf terms from each document.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.