R dividing texts in tm package - recognizing speakers - regex

Question

I am trying to identify the most frequently used words in the congress speeches, and have to separate them by the congressperson. I am just starting to learn about R and the tm package. I have a code that can find the most frequent words, but what kind of a code can I use to automatically identify and store the speaker of the speech?
Text looks like this:
OPENING STATEMENT OF SENATOR HERB KOHL, CHAIRMAN
The Chairman. Good afternoon to everybody, and thank you
very much for coming to this hearing this afternoon.
In today's tough economic climate, millions of seniors have
lost a big part of their retirement and investments in only a
matter of months. Unlike younger Americans, they do not have
time to wait for the markets to rebound in order to recoup a
lifetime of savings.
[....]
STATEMENT OF SENATOR MEL MARTINEZ, RANKING MEMBER
[....]
I would like to be able to get these names, or separate text by the people. Hope you can help me. Thanks a lot.

Answer 1

Would it be correct to say that you want to split the file so you have one text object per speaker? And then use a regular expression to grab the speaker's name for each object? Then you can write a function to collect word frequencies, etc. on each object and put them in a table where the row or column names are the speaker's names.
If so, you might say x is your text, then use strsplit(x, "STATEMENT OF") to split on the words STATEMENT OF, then grep() or str_extract() to return the 2 or 3 words after SENATOR (do they always have only two names as in your example?).
Have a look here for more on the use of these functions, and text manipulation in general in R: http://en.wikibooks.org/wiki/R_Programming/Text_Processing
UPDATE Here's a more complete answer...
#create object containing all text
x <- c("OPENING STATEMENT OF SENATOR HERB KOHL, CHAIRMAN
The Chairman. Good afternoon to everybody, and thank you
very much for coming to this hearing this afternoon.
In today's tough economic climate, millions of seniors have
lost a big part of their retirement and investments in only a
matter of months. Unlike younger Americans, they do not have
time to wait for the markets to rebound in order to recoup a
lifetime of savings.
STATEMENT OF SENATOR BIG APPLE KOHL, CHAIRMAN
I am trying to identify the most frequently used words in the
congress speeches, and have to separate them by the congressperson.
I am just starting to learn about R and the tm package. I have a code
that can find the most frequent words, but what kind of a code can I
use to automatically identify and store the speaker of the speech
STATEMENT OF SENATOR LITTLE ORANGE, CHAIRMAN
Would it be correct to say that you want
to split the file so you have one text object
per speaker? And then use a regular expression
to grab the speaker's name for each object? Then
you can write a function to collect word frequencies,
etc. on each object and put them in a table where the
row or column names are the speaker's names.")
# split object on first two words
y <- unlist(strsplit(x, "STATEMENT OF"))
#load library containing handy function
library(stringr)
# use word() to return words in positions 3 to 4 of each string, which is where the first and last names are
z <- word(y[2:4], 3, 4) # note that the first line in the character vector y has only one word and this function gives and error if there are not enough words in the line
z # have a look at the result...
[1] "HERB KOHL," "BIG APPLE" "LITTLE ORANGE,"
No doubt a regular expressions wizard could come up with something to do it quicker and neater!
Anyway, from here you can run a function to calculate word freqs on each line in the vector y (ie. each speaker's speech) and then make another object that combines the word freq results with the names for further analysis.

Answer 2

This is how I'd approach it using Ben's example (use qdap to parse and create a dataframe and then convert to a Corpus with 3 documents; note that qdap was designed for transcript data like this and a Corpus may not be the best data format):
library(qdap)
dat <- unlist(strsplit(x, "\\n"))
locs <- grep("STATEMENT OF ", dat)
nms <- sapply(strsplit(dat[locs], "STATEMENT OF |,"), "[", 2)
dat[locs] <- "SPLIT_HERE"
corp <- with(data.frame(person=nms, dialogue =
Trim(unlist(strsplit(paste(dat[-1], collapse=" "), "SPLIT_HERE")))),
df2tm_corpus(dialogue, person))
tm::inspect(corp)
## A corpus with 3 text documents
##
## The metadata consists of 2 tag-value pairs and a data frame
## Available tags are:
## create_date creator
## Available variables in the data frame are:
## MetaID
##
## $`SENATOR BIG APPLE KOHL`
## I am trying to identify the most frequently used words in the congress speeches, and have to separate them by the congressperson. I am just starting to learn about R and the tm package. I have a code that can find the most frequent words, but what kind of a code can I use to automatically identify and store the speaker of the speech
##
## $`SENATOR HERB KOHL`
## The Chairman. Good afternoon to everybody, and thank you very much for coming to this hearing this afternoon. In today's tough economic climate, millions of seniors have lost a big part of their retirement and investments in only a matter of months. Unlike younger Americans, they do not have time to wait for the markets to rebound in order to recoup a lifetime of savings.
##
## $`SENATOR LITTLE ORANGE`
## Would it be correct to say that you want to split the file so you have one text object per speaker? And then use a regular expression to grab the speaker's name for each object? Then you can write a function to collect word frequencies, etc. on each object and put them in a table where the row or column names are the speaker's names.