Create dfm step by step with quanteda

Question

I want to analyze a big (n=500,000) corpus of documents. I am using quanteda in the expectation that will be faster than tm_map() from tm

Answer 1

We designed dfm() not as a "black box" but more as a Swiss army knife that combines many of the options that typical users want to apply when converting their texts to a matrix of documents and features. However all of these options are also available through lower-level processing commands, should you wish to exert a finer level of control.

However one of the design principles of quanteda is that text only becomes "features" through the process of tokenisation. If you have a set of tokenised features that you wish to exclude, you must first tokenise your text, or you cannot exclude them. Unlike other text packages for R (e.g. tm), these steps are applied "downstream" from a corpus, so that the corpus remains an unprocessed set of texts to which manipulations will be applied (but will not itself be a transformed set of texts). The purpose of this is to preserve generality but also to promote reproducibility and transparency in text analysis.

In response to your questions:

1. You can however override our encouraged behaviour using the texts(myCorpus) <- function, where what is assigned to the texts will override the existing texts. So you could use regular expressions to remove punctuation and numbers -- for example the stringi commands and using the Unicode classes for punctuation and numerals to identify patterns.

2. I would recommend you tokenise before removing stopwords. Stop "words" are tokens, so there is no way to remove these from the text before you tokenise the text. Even applying regular expressions to substitute them for "" involves specifying some form of word boundary in the regex - again, this is tokenisation.

3. To tokenise into unigrams and bigrams:

   tokens(myCorpus, ngrams = 1:2)

4. To create the dfm, simply call dfm(myTokens). (You could also have applied step 3, for ngrams, at this stage.

Bonus 1: n=2 collocations produces the same list as bigrams, except in a different format. Did you intend something else? (Separate SO question perhaps?)

Bonus 2: See dfm_trim(x, sparsity = ). The removeSparseTerms() options are quite confusing to most people, but this included for migrants from tm. See this post for a full explanation.

BTW: Use texts() instead of ie2010Corpus$documents$texts -- we will rewrite the object structure of a corpus soon, so you should not access its internals this way when there is an extractor function. (Also, this step is unnecessary - here you have simply recreated the corpus.)

Update 2018-01:

The new name for the corpus object is data_corpus_irishbudget2010, and the collocation scoring function is textstat_collocations().