Thank you very much for all your comments, and sorry for the confusion
of my messages. My corpus is a collection of responses to an open
question from a questionnaire. Since my intention is not to create
groups of respondents but to treat all responses as a "whole
discourse" on a particular issue so that I can find out different
"semantic contexts" within the text. I have all the responses in a
single document, then I want to split it into strings of (specified) n
words. The resulting semantic contexts would be sets of (correlated)
word-strings containing particularly relevant (correlated) words.
I guess I must dive deeper into the "ca" and "tm" packages. Any other
ideas will be really welcomed.
best,
Pep Vallbé
On Mar 30, 2009, at 2:05 PM, Alan Zaslavsky wrote:
Maybe not terribly hard, depending on exactly what you need.
Suppose you turn your text into a character vector 'mytext' of
words. Then for a table of words appearing delta words apart
(ordered), you can table mytext against itself with a lag:
nwords=length(mytext)
burttab=table(mytext[-(1:delta)],mytext[nwords+1-(1:delta)])
Add to its transpose and sum over delta up to your maximum distance
apart. If you want only words appearing near each other within the
same sentence (or some other unit), pad out the sentence break with
at least delta instances of a dummy spacer:
the cat chased the greedy rat SPACER SPACER SPACER the dog chased
the
clever cat
This will count all pairings at distance delta; if you want to count
only those for which this was the NEAREST co-occurence (so
the cat and the rate chased the dog
would count as two at delta=3 but not one at delta=6) it will be
trickier and I'm not sure this approach can be modified to handle it.
Date: Sun, 29 Mar 2009 22:20:15 -0400
From: "Murray Cooper" <myrm...@earthlink.net>
Subject: Re: [R] Burt table from word frequency list
The usual approach is to count the co-occurence within so many
words of
each other. Typical is between 5 words before and 5 words after a
given word. So for each word in the document, you look for the
occurence of all other words within -5 -4 -3 -2 -1 0 1 2 3 4 5 words.
Depending on the language and the question being asked certain words
may be excluded.
This is not a simple function! I don't know if anyone has done a
package, for this type of analysis but with over 2000 packages
floating
around you might get lucky.
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