In quanteda/quanteda: Quantitative Analysis of Textual Data

knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "##"
)

quanteda has the functionality to select, remove or compound multi-word expressions, such as phrasal verbs ("try on", "wake up" etc.) and place names ("New York", "South Korea" etc.).

library(quanteda)

Tokenize

toks <- tokens(data_corpus_inaugural)

Define multi-word expressions

Functions for tokens objects take a character vector, a dictionary or collocations as pattern. All those three can be used for multi-word expressions, but you have to be aware their differences.

Character vector

The most basic way to define multi-word expressions is separating words by whitespaces and wrap the character vector by phrase().

multiword <- c("United States", "New York")

Keyword-in-context

kwic() is useful to find multi-word expressions in tokens. If you are not sure if "United" and "States" are separated, check their positions (e.g. "434:435").

head(kwic(toks, pattern = phrase(multiword)))

Select tokens

Similarly, you can select or remove multi-word expression using tokens_select().

head(tokens_select(toks, pattern = phrase(multiword)))

Compound tokens

tokens_compound() joins elements of multi-word expressions by underscore, so they become "United_States" and "New_York".

comp_toks <- tokens_compound(toks, pattern = phrase(multiword))
head(tokens_select(comp_toks, pattern = c("United_States", "New_York")))

Dictionary

Elements of multi-word expressions should be separately by whitespaces in a dictionary, but you do not use phrase() here.

dict_multiword <- dictionary(list(country = "United States", 
                                  city = "New York"))

Lookup dictionary

head(tokens_lookup(toks, dictionary = dict_multiword))

Collocations

With textstat_collocations(), it is possible to discover multi-word expressions through statistical scoring of the associations of adjacent words.

Discover collocations

If textstat_collocations() is applied to a tokens object comprised only of capitalize words, it usually returns multi-word proper names.

library("quanteda.textstats")
col <- toks |> 
    tokens_remove(stopwords("en")) |> 
    tokens_select(pattern = "^[A-Z]", valuetype = "regex", 
                  case_insensitive = FALSE, padding = TRUE) |>
    textstat_collocations(min_count = 5, tolower = FALSE)
head(col)

Compound collocations

Collocations are automatically recognized as multi-word expressions by tokens_compound() in case-sensitive fixed pattern matching. This is the fastest way to compound large numbers of multi-word expressions, but make sure that tolower = FALSE in textstat_collocations() to do this.

comp_toks2 <- tokens_compound(toks, pattern = col)
head(kwic(comp_toks2, pattern = c("United_States", "New_York")))

You can use phrase() on collocations if more flexibility is needed. This is usually the case if you compound tokens from different corpus.

comp_toks3 <- tokens_compound(toks, pattern = phrase(col$collocation))
head(kwic(comp_toks3, pattern = c("United_States", "New_York")))

quanteda/quanteda documentation built on April 15, 2024, 7:59 a.m.