Corpus · TextAnalysis

A Corpus may contain many different types of documents. It is generally more convenient to standardize all of the documents in a corpus using a single type. This can be done using the standardize! function:

TextAnalysis.standardize! — Function

standardize!(crps::Corpus, ::Type{T}) where T <: AbstractDocument

Standardize the documents in a Corpus to a common type.

Example

julia> crps = Corpus([StringDocument("Document 1"),
		              TokenDocument("Document 2"),
		              NGramDocument("Document 3")])
A Corpus with 3 documents:
 * 1 StringDocument's
 * 0 FileDocument's
 * 1 TokenDocument's
 * 1 NGramDocument's

Corpus's lexicon contains 0 tokens
Corpus's index contains 0 tokens


julia> standardize!(crps, NGramDocument)

# After this step, you can check that the corpus only contains NGramDocument's:

julia> crps
A Corpus with 3 documents:
 * 0 StringDocument's
 * 0 FileDocument's
 * 0 TokenDocument's
 * 3 NGramDocument's

Corpus's lexicon contains 0 tokens
Corpus's index contains 0 tokens

source

Processing a Corpus

We can apply the same sort of preprocessing steps that are defined for individual documents to an entire corpus at once:

julia> using TextAnalysis
julia> crps = Corpus([StringDocument("Document ..!!"),
                      StringDocument("Document ..!!")])A Corpus with 2 documents:
 * 2 StringDocument's
 * 0 FileDocument's
 * 0 TokenDocument's
 * 0 NGramDocument's

Corpus's lexicon contains 0 tokens
Corpus's index contains 0 tokens
julia> prepare!(crps, strip_punctuation)
julia> text(crps[1])"Document "
julia> text(crps[2])"Document "

These operations are run on each document in the corpus individually.

Corpus Statistics

Often we wish to think broadly about properties of an entire corpus at once. In particular, we want to work with two constructs:

Lexicon: The lexicon of a corpus consists of all the terms that occur in any document in the corpus. The lexical frequency of a term tells us how often a term occurs across all of the documents. Often the most interesting words in a document are those words whose frequency within a document is higher than their frequency in the corpus as a whole.
Inverse Index: If we are interested in a specific term, we often want to know which documents in a corpus contain that term. The inverse index tells us this and therefore provides a simplistic sort of search algorithm.

Because computations involving the lexicon can take a long time, a Corpus's default lexicon is blank:

julia> crps = Corpus([StringDocument("Name Foo"),
                          StringDocument("Name Bar")])
julia> lexicon(crps)
Dict{String,Int64} with 0 entries

In order to work with the lexicon, you have to update it and then access it:

julia> update_lexicon!(crps)

julia> lexicon(crps)
Dict{String,Int64} with 3 entries:
  "Bar"    => 1
  "Foo"    => 1
  "Name" => 2

But once this work is done, you can easier address lots of interesting questions about a corpus:

julia> lexical_frequency(crps, "Name")
0.5

julia> lexical_frequency(crps, "Foo")
0.25

Like the lexicon, the inverse index for a corpus is blank by default:

julia> inverse_index(crps)
Dict{String,Array{Int64,1}} with 0 entries

Again, you need to update it before you can work with it:

julia> update_inverse_index!(crps)

julia> inverse_index(crps)
Dict{String,Array{Int64,1}} with 3 entries:
  "Bar"    => [2]
  "Foo"    => [1]
  "Name" => [1, 2]

But once you've updated the inverse index, you can easily search the entire corpus:

julia> crps["Name"]

2-element Array{Int64,1}:
 1
 2

julia> crps["Foo"]
1-element Array{Int64,1}:
 1

julia> crps["Summer"]
0-element Array{Int64,1}

Converting a DataFrame from a Corpus

Sometimes we want to apply non-text specific data analysis operations to a corpus. The easiest way to do this is to convert a Corpus object into a DataFrame:

convert(DataFrame, crps)

Corpus Metadata

You can also retrieve the metadata for every document in a Corpus at once:

languages(): What language is the document in? Defaults to Languages.English(), a Language instance defined by the Languages package.
titles(): What is the title of the document? Defaults to "Untitled Document".
authors(): Who wrote the document? Defaults to "Unknown Author".
timestamps(): When was the document written? Defaults to "Unknown Time".

julia> crps = Corpus([StringDocument("Name Foo"),
                                 StringDocument("Name Bar")])

julia> languages(crps)
2-element Array{Languages.English,1}:
 Languages.English()
 Languages.English()

julia> titles(crps)
2-element Array{String,1}:
 "Untitled Document"
 "Untitled Document"

julia> authors(crps)
2-element Array{String,1}:
 "Unknown Author"
 "Unknown Author"

julia> timestamps(crps)
2-element Array{String,1}:
 "Unknown Time"
 "Unknown Time"

It is possible to change the metadata fields for each document in a Corpus. These functions use the same metadata value for every document:

julia> languages!(crps, Languages.German())
julia> titles!(crps, "")
julia> authors!(crps, "Me")
julia> timestamps!(crps, "Now")

Additionally, you can specify the metadata fields for each document in a Corpus individually:

julia> languages!(crps, [Languages.German(), Languages.English
julia> titles!(crps, ["", "Untitled"])
julia> authors!(crps, ["Ich", "You"])
julia> timestamps!(crps, ["Unbekannt", "2018"])

Creating a Corpus

Standardizing a Corpus

Processing a Corpus

Corpus Statistics

Converting a DataFrame from a Corpus

Corpus Metadata