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.
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.
