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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.

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  • $\begingroup$ 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 '14 at 23:39
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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.

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