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:


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, ] %>% %>% rename(count=".") %>%
  mutate(token=row.names(.)) %>% arrange(-count)

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

get.doc.freq<- function(dtm, token)
  dtm[, token] %>% %>% 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:


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


Or to get the document frequency of the word:


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

Continue reading “Document-Term Matrix: Text Mining in R and Python”

Python Package for Short Text Mining

There has been a lot of methods for natural language processing and text mining. However, in tweets, surveys, Facebook, or many online data, texts are short, lacking data to build enough information. Traditional bag-of-words (BOW) model gives sparse vector representation.

Semantic relations between words are important, because we usually do not have enough data to capture the similarity between words. We do not want “drive” and “drives,” or “driver” and “chauffeur” to be completely different.

The relation between or order of words become important as well. Or we want to capture the concepts that may be correlated in our training dataset.

We have to represent these texts in a special way and perform supervised learning with traditional machine learning algorithms or deep learning algorithms.

This package `shorttext‘ was designed to tackle all these problems. It is not a completely new invention, but putting everything known together. It contains the following features:

  • example data provided (including subject keywords and NIH RePORT);
  • text preprocessing;
  • pre-trained word-embedding support;
  • gensim topic models (LDA, LSI, Random Projections) and autoencoder;
  • topic model representation supported for supervised learning using scikit-learn;
  • cosine distance classification;¬†and
  • neural network classification (including ConvNet, and C-LSTM).

Readers can refer this to the documentation.

Continue reading “Python Package for Short Text Mining”

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