Document-Term Matrix: Text Mining in R and Python

In text mining, it is important to create the document-term matrix (DTM) of the corpus we are interested in. A DTM is basically a matrix, with documents designated by rows and words by columns, that the elements are the counts or the weights (usually by tf-idf). Subsequent analysis is usually based creatively on DTM.

Exploring with DTM therefore becomes an important issues with a good text-mining tool. How do we perform exploratory data analysis on DTM using R and Python? We will demonstrate it using the data set of U. S. Presidents’ Inaugural Address, preprocessed, and can be downloaded here.

R: textmineR

In R, we can use the package textmineR, which has been in introduced in a previous post. Together with other packages such as dplyr (for tidy data analysis) and snowBall (for stemming), load all of them at the beginning:

library(dplyr)
library(textmineR)
library(SnowballC)

Load the datasets:

usprez.df<- read.csv('inaugural.csv', stringsAsFactors = FALSE)

Then we create the DTM, while we remove all digits and punctuations, make all letters lowercase, and stem all words using Porter stemmer.

dtm<- CreateDtm(usprez.df$speech,
                doc_names = usprez.df$yrprez,
                ngram_window = c(1, 1),
                lower = TRUE,
                remove_punctuation = TRUE,
                remove_numbers = TRUE,
                stem_lemma_function = wordStem)

Then defining a set of functions:

get.doc.tokens<- function(dtm, docid)
  dtm[docid, ] %>% as.data.frame() %>% rename(count=".") %>%
  mutate(token=row.names(.)) %>% arrange(-count)

get.token.occurrences<- function(dtm, token)
  dtm[, token] %>% as.data.frame() %>% rename(count=".") %>%
  mutate(token=row.names(.)) %>% arrange(-count)

get.total.freq<- function(dtm, token) dtm[, token] %>% sum

get.doc.freq<- function(dtm, token)
  dtm[, token] %>% as.data.frame() %>% rename(count=".") %>%
  filter(count>0) %>% pull(count) %>% length

Then we can happily extract information. For example, if we want to get the top-most common words in 2009’s Obama’s speech, enter:

dtm %>% get.doc.tokens('2009-Obama') %>% head(10)

Or which speeches have the word “change”: (but need to stem the word before extraction)

dtm %>% get.token.occurrences(wordStem('change')) %>% head(10)

You can also get the total number of occurrence of the words by:

dtm %>% get.doc.freq(wordStem('change'))   # gives 28

Python: shorttext

In Python, similar things can be done using the package shorttext, described in a previous post. It uses other packages such as pandas and stemming. Load all packages first:

import shorttext
import numpy as np
import pandas as pd
from stemming.porter import stem

import re

And define the preprocessing pipelines:

pipeline = [lambda s: re.sub('[^\w\s]', '', s),
            lambda s: re.sub('[\d]', '', s),
            lambda s: s.lower(),
            lambda s: ' '.join(map(stem, shorttext.utils.tokenize(s)))
 ]
txtpreproceesor = shorttext.utils.text_preprocessor(pipeline)

The function <code>txtpreprocessor</code> above perform the functions we talked about in R.

Load the dataset:

usprezdf = pd.read_csv('inaugural.csv')

The corpus needs to be preprocessed before putting into the DTM:

docids = list(usprezdf['yrprez'])    # defining document IDs
corpus = [txtpreproceesor(speech).split(' ') for speech in usprezdf['speech']]

Then create the DTM:

dtm = shorttext.utils.DocumentTermMatrix(corpus, docids=docids, tfidf=False)

Then we do the same thing as we have done above. To get the top-most common words in 2009’s Obama’s speech, enter:

dtm.get_doc_tokens('2009-Obama')

Or we look up which speeches have the word “change”:

dtm.get_token_occurences(stem('change'))

Or to get the document frequency of the word:

dtm.get_doc_frequency(stem('change'))

They Python and R codes give different document frequencies probably because the two stemmers work slightly differently.

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