Introduction

This tutorial introduces keyness and keyword analysis with R and shows how to extract and visualize keywords.

This tutorial is aimed at beginners and intermediate users of R with the aim of showcasing how to extract keywords from and analyze keywords in textual data using R. The aim is not to provide a fully-fledged analysis but rather to show and exemplify selected useful methods associated with keyness and keyword analysis.

To be able to follow this tutorial, we suggest you check out and familiarize yourself with the content of the following R Basics tutorials:

Click here1 to download the entire R Notebook for this tutorial.

Binder
Click here to open an interactive Jupyter notebook that allows you to execute, change, and edit the code as well as to upload your own data.


LADAL TOOL

Click on this Binder badge to open an notebook-based tool
that calculates keyness statistics and allows you to download the results.




How can you detect keywords, i.e. words that are characteristic of a text (or a collection of texts)?


This tutorial aims to show how you can answer this question.

Preparation and session set up

This tutorial is based on R. If you have not installed R or are new to it, you will find an introduction to and more information how to use R here. For this tutorials, we need to install certain packages from an R library so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead and ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time).

# set options
options(stringsAsFactors = F)
options(scipen = 999)
options(max.print=1000)
# install packages
install.packages("flextable")
install.packages("Matrix")
install.packages("quanteda")
install.packages("quanteda.textstats")
install.packages("quanteda.textplots")
install.packages("dplyr")
install.packages("stringr")
install.packages("tm")
install.packages("sna")
install.packages("tidytext")
install.packages("ggplot2")
# install klippy for copy-to-clipboard button in code chunks
install.packages("remotes")
remotes::install_github("rlesur/klippy")

Next, we load the packages.

# load packages
library(flextable)
library(Matrix)
library(quanteda)
library(quanteda.textstats)
library(quanteda.textplots)
library(dplyr)
library(stringr)
library(tm)
library(sna)
library(ggplot2)
# activate klippy for copy-to-clipboard button
klippy::klippy()

Keywords

Keywords play a pivotal role in text analysis, serving as distinctive terms that hold particular significance within a given text, context, or collection. These words stand out due to their heightened frequency in a specific text or context, setting them apart from their occurrence in another. In essence, keywords are linguistic markers that encapsulate the essence or topical focus of a document or dataset. The process of identifying keywords involves a methodology akin to the one employed for detecting collocations using kwics. This entails comparing the use of a particular word in corpus A, against its use in corpus B. By discerning the frequency disparities, we gain valuable insights into the salient terms that contribute significantly to the unique character and thematic emphasis of a given text or context.

LADAL TOOL

Click on this Binder badge to open an notebook-based tool
that calculates keyness measures and allows you to download the results.



Dimensions of keyness

Before we start with the practical part of this tutorial, it is important to talk about the different dimensions of keyness (see Sönning 2023).

Keyness analysis identifies typical items in a discourse domain, where typicalness traditionally relates to frequency of occurrence. The emphasis is on items used more frequently in the target corpus compared to a reference corpus. Egbert and Biber (2019) expanded this notion, highlighting two criteria for typicalness: content-distinctiveness and content-generalizability.

  • Content-distinctiveness refers to an item’s association with the domain and its topical relevance.

  • Content-generalizability pertains to an item’s widespread usage across various texts within the domain.

These criteria bridge traditional keyness approaches with broader linguistic perspectives, emphasizing both the distinctiveness and generalizability of key items within a corpus.

Following Sönning (2023), we adopt Egbert and Biber (2019) keyness criteria, distinguishing between frequency-oriented and dispersion-oriented approaches to assess keyness. These perspectives capture distinct, linguistically meaningful attributes of typicalness. We also differentiate between keyness features inherent to the target variety and those that emerge from comparing it to a reference variety. This four-way classification, detailed in the table below, links methodological choices to the linguistic meaning conveyed by quantitative measures. Typical items exhibit a sufficiently high occurrence rate to be discernible in the target variety, with discernibility measured solely within the target corpus. Key items are also distinct, being used more frequently than in reference domains of language use. While discernibility and distinctiveness both rely on frequency, they measure different aspects of typicalness.

Dimensions of keyness (see Soenning, 2023: 3)

Analysis

Frequency.oriented

Dispersion.oriented

Target variety in isolation

Discernibility of item in the target variety

Generality across texts in the target variety

Comparison to reference variety

Distinctiveness relative to the reference variety

Comparative generality relative to the reference variety

The second aspect of keyness involves an item’s dispersion across texts in the target domain, indicating its widespread use. A typical item should appear evenly across various texts within the target domain, reflecting its generality. This breadth of usage can be compared to its occurrence in the reference domain, termed as comparative generality. Therefore, a key item should exhibit greater prevalence across target texts compared to those in the reference domain.

Identifying keywords

Here, we focus on a frequency-based approach that assesses distinctiveness relative to the reference variety. To identify these keywords, we can follow the procedure we have used to identify collocations using kwics - the idea is essentially identical: we compare the use of a word in a target corpus A to its use in a reference corpus.

To determine if a token is a keyword and if it occurs significantly more frequently in a target corpus compared to a reference corpus, we use the following information (that is provided by the table above):

  • O11 = Number of times wordx occurs in target corpus

  • O12 = Number of times wordx occurs in reference corpus (without target corpus)

  • O21 = Number of times other words occur in target corpus

  • O22 = Number of times other words occur in reference corpus

Example:

target corpus reference corpus
token O11 O12 = R1
other tokens O21 O22 = R2
= C1 = C2 = N

We begin with loading two texts (text1 is our target and text2 is our reference).

# load data
text1 <- base::readRDS(url("https://slcladal.github.io/data/orwell.rda", "rb")) %>%
  paste0(collapse = " ")
text2 <- base::readRDS(url("https://slcladal.github.io/data/melville.rda", "rb"))  %>%
  paste0(collapse = " ")
First 200 characters of text 1

.

1984 George Orwell Part 1, Chapter 1 It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind, sli

As you can see, text1 is George Orwell’s 1984.

First 200 characters of text 2

.

MOBY-DICK; or, THE WHALE. By Herman Melville CHAPTER 1. Loomings. Call me Ishmael. Some years ago—never mind how long precisely—having little or no money in my purse, and nothing particular to interes

The table shows that text2 is Herman Melville’s Moby Dick.

After loading the two texts, we create a frequency table of first text.

text1_words <- text1 %>%
  # remove non-word characters
  stringr::str_remove_all("[^[:alpha:] ]") %>%
  # convert to lower
  tolower() %>%
  # tokenize the corpus files
  quanteda::tokens(remove_punct = T, 
                   remove_symbols = T,
                   remove_numbers = T) %>%
  # unlist the tokens to create a data frame
  unlist() %>%
  as.data.frame() %>%
  # rename the column to 'token'
  dplyr::rename(token = 1) %>%
  # group by 'token' and count the occurrences
  dplyr::group_by(token) %>%
  dplyr::summarise(n = n()) %>%
  # add column stating where the frequency list is 'from'
  dplyr::mutate(type = "text1")

Now, we create a frequency table of second text.

text2_words <- text2 %>%
  # remove non-word characters
  stringr::str_remove_all("[^[:alpha:] ]") %>%
  # convert to lower
  tolower() %>%
  # tokenize the corpus files
  quanteda::tokens(remove_punct = T, 
                   remove_symbols = T,
                   remove_numbers = T) %>%
  # unlist the tokens to create a data frame
  unlist() %>%
  as.data.frame() %>%
  # rename the column to 'token'
  dplyr::rename(token = 1) %>%
  # group by 'token' and count the occurrences
  dplyr::group_by(token) %>%
  dplyr::summarise(n = n()) %>%
  # add column stating where the frequency list is 'from'
  dplyr::mutate(type = "text2")

In a next step, we combine the tables.

texts_df <- dplyr::left_join(text1_words, text2_words, by = c("token")) %>%
  # rename columns and select relevant columns
  dplyr::rename(text1 = n.x,
                text2 = n.y) %>%
  dplyr::select(-type.x, -type.y) %>%
  # replace NA values with 0 in 'corpus' and 'kwic' columns
  tidyr::replace_na(list(text1 = 0, text2 = 0))
Frequency table of tokens in text1 and text2

token

text1

text2

a

2,390

4,536

aaronson

8

0

aback

2

2

abandon

3

3

abandoned

4

7

abashed

1

2

abbreviated

1

0

abiding

1

1

ability

1

1

abject

3

0

We now calculate the frequencies of the observed and expected frequencies as well as the row and column totals.

texts_df %>%
  dplyr::mutate(text1 = as.numeric(text1),
                text2 = as.numeric(text2)) %>%
  dplyr::mutate(C1 = sum(text1),
                C2 = sum(text2),
                N = C1 + C2) %>%
  dplyr::rowwise() %>%
  dplyr::mutate(R1 = text1+text2,
                R2 = N - R1,
                O11 = text1,
                O12 = R1-O11,
                O21 = C1-O11,
                O22 = C2-O12) %>%
  dplyr::mutate(E11 = (R1 * C1) / N,
                E12 = (R1 * C2) / N,
                E21 = (R2 * C1) / N,
                E22 = (R2 * C2) / N) %>%
  dplyr::select(-text1, -text2) -> stats_tb2
First 10 rows of the processed frequency table

token

C1

C2

N

R1

R2

O11

O12

O21

O22

E11

E12

E21

E22

a

94,677

169,163

263,840

6,926

256,914

2,390

4,536

92,287

164,627

2,485.3430185

4,440.6569815

92,191.66

164,722.3

aaronson

94,677

169,163

263,840

8

263,832

8

0

94,669

169,163

2.8707398

5.1292602

94,674.13

169,157.9

aback

94,677

169,163

263,840

4

263,836

2

2

94,675

169,161

1.4353699

2.5646301

94,675.56

169,160.4

abandon

94,677

169,163

263,840

6

263,834

3

3

94,674

169,160

2.1530549

3.8469451

94,674.85

169,159.2

abandoned

94,677

169,163

263,840

11

263,829

4

7

94,673

169,156

3.9472673

7.0527327

94,673.05

169,155.9

abashed

94,677

169,163

263,840

3

263,837

1

2

94,676

169,161

1.0765274

1.9234726

94,675.92

169,161.1

abbreviated

94,677

169,163

263,840

1

263,839

1

0

94,676

169,163

0.3588425

0.6411575

94,676.64

169,162.4

abiding

94,677

169,163

263,840

2

263,838

1

1

94,676

169,162

0.7176850

1.2823150

94,676.28

169,161.7

ability

94,677

169,163

263,840

2

263,838

1

1

94,676

169,162

0.7176850

1.2823150

94,676.28

169,161.7

abject

94,677

169,163

263,840

3

263,837

3

0

94,674

169,163

1.0765274

1.9234726

94,675.92

169,161.1

We can now calculate the keyness measures.

stats_tb2 %>%
  # determine number of rows
  dplyr::mutate(Rws = nrow(.)) %>%   
  # work row-wise
    dplyr::rowwise() %>%
    # calculate fishers' exact test
    dplyr::mutate(p = as.vector(unlist(fisher.test(matrix(c(O11, O12, O21, O22), 
                                                        ncol = 2, byrow = T))[1]))) %>%

# extract descriptives
    dplyr::mutate(ptw_target = O11/C1*1000,
                  ptw_ref = O12/C2*1000) %>%
    
    # extract x2 statistics
    dplyr::mutate(X2 = (O11-E11)^2/E11 + (O12-E12)^2/E12 + (O21-E21)^2/E21 + (O22-E22)^2/E22) %>%
    
    # extract keyness measures
    dplyr::mutate(phi = sqrt((X2 / N)),
                  MI = log2(O11 / E11),
                  t.score = (O11 - E11) / sqrt(O11),
                  PMI = log2( (O11 / N) / ((O11+O12) / N) * 
                                ((O11+O21) / N) ),
                  DeltaP = (O11 / R1) - (O21 / R2),
                  LogOddsRatio = log(((O11 + 0.5) * (O22 + 0.5))  / ( (O12 + 0.5) * (O21 + 0.5) )),
                  G2 = 2 * ((O11+ 0.001) * log((O11+ 0.001) / E11) + (O12+ 0.001) * log((O12+ 0.001) / E12) + O21 * log(O21 / E21) + O22 * log(O22 / E22)),
                  
                  # traditional keyness measures
                  RateRatio = ((O11+ 0.001)/(C1*1000)) / ((O12+ 0.001)/(C2*1000)),
                  RateDifference = (O11/(C1*1000)) - (O12/(C2*1000)),
                  DifferenceCoefficient = RateDifference / sum((O11/(C1*1000)), (O12/(C2*1000))),
                  OddsRatio = ((O11 + 0.5) * (O22 + 0.5))  / ( (O12 + 0.5) * (O21 + 0.5) ),
                  LLR = 2 * (O11 * (log((O11 / E11)))),
                  RDF = abs((O11 / C1) - (O12 / C2)),
                  PDiff = abs(ptw_target - ptw_ref) / ((ptw_target + ptw_ref) / 2) * 100,
                  SignedDKL = sum(ifelse(O11 > 0, O11 * log(O11 / ((O11 + O12) / 2)), 0) - ifelse(O12 > 0, O12 * log(O12 / ((O11 + O12) / 2)), 0))) %>%
    
    # determine Bonferroni corrected significance
    dplyr::mutate(Sig_corrected = dplyr::case_when(p / Rws > .05 ~ "n.s.",
                                                   p / Rws > .01 ~ "p < .05*",
                                                   p / Rws > .001 ~ "p < .01**",
                                                   p / Rws <= .001 ~ "p < .001***",
                                                   T ~ "N.A.")) %>% 
    # round p-value
    dplyr::mutate(p = round(p, 5),
                  type = ifelse(E11 > O11, "antitype", "type"),
                  phi = ifelse(E11 > O11, -phi, phi),
                  G2 = ifelse(E11 > O11, -G2, G2)) %>%
    # filter out non significant results
    dplyr::filter(Sig_corrected != "n.s.") %>%
    # arrange by G2
    dplyr::arrange(-G2) %>%
    # remove superfluous columns
    dplyr::select(-any_of(c("TermCoocFreq", "AllFreq", "NRows", 
                            "R1", "R2", "C1", "C2", "E12", "E21",
                            "E22", "upp", "low", "op", "t.score", "z.score", "Rws"))) %>%
    dplyr::relocate(any_of(c("token", "type", "Sig_corrected", "O11", "O12",
                             "ptw_target", "ptw_ref", "G2",  "RDF", "RateRatio", 
                             "RateDifference", "DifferenceCoefficient", "LLR", "SignedDKL",
                             "PDiff", "LogOddsRatio", "MI", "PMI", "phi", "X2",  
                             "OddsRatio", "DeltaP", "p", "E11", "O21", "O22"))) -> assoc_tb3
First 10 rows of the keyness statistic table

token

type

Sig_corrected

O11

O12

ptw_target

ptw_ref

G2

RDF

RateRatio

RateDifference

DifferenceCoefficient

LLR

SignedDKL

PDiff

LogOddsRatio

MI

PMI

phi

X2

OddsRatio

DeltaP

p

E11

O21

O22

N

winston

type

p < .001***

440

0

4.6473800

0.00000000

903.1799

0.0046473800

786,166.536360

0.0000046473800

1.0000000

901.8871

304.98476

200.00000

7.3661051

1.4785774

-1.478577

0.05463223

787.4780

1,581.462194

0.6422285

0

157.89069

94,237

169,163

263,840

was

type

p < .001***

2,146

1,618

22.6665399

9.56473933

703.9743

0.0131018006

2.369802

0.0000131018006

0.4064933

1,987.1753

526.25807

81.29867

0.8760446

0.6679609

-2.289194

0.05299452

740.9733

2.401383

0.2143537

0

1,350.68310

92,531

167,545

263,840

had

type

p < .001***

1,268

765

13.3929043

4.52226551

591.3677

0.0088706388

2.961546

0.0000088706388

0.4951468

1,401.9010

497.77101

99.02936

1.0944013

0.7975219

-2.159633

0.04865982

624.7146

2.987394

0.2669231

0

729.52676

93,409

168,398

263,840

party

type

p < .001***

250

9

2.6405568

0.05320312

442.6871

0.0025873537

49.626297

0.0000025873537

0.9604990

494.7523

188.44312

192.09980

3.8551473

1.4275534

-1.529601

0.03962998

414.3699

47.235573

0.6070044

0

92.94020

94,427

169,154

263,840

he

type

p < .001***

1,889

1,729

19.9520475

10.22091119

406.2806

0.0097311363

1.952081

0.0000097311363

0.3225118

1,416.7423

159.94782

64.50237

0.6787494

0.5410077

-2.416147

0.04013583

425.0158

1.971411

0.1655392

0

1,298.29209

92,788

167,434

263,840

obrien

type

p < .001***

178

0

1.8800765

0.00000000

365.0499

0.0018800765

318,041.162722

0.0000018800765

1.0000000

364.8543

123.38020

200.00000

6.4600069

1.4785774

-1.478577

0.03473095

318.2541

639.065492

0.6415904

0

63.87396

94,499

169,163

263,840

she

type

p < .001***

378

110

3.9925219

0.65026040

352.4095

0.0033422615

6.139842

0.0000033422615

0.7198833

581.7046

253.09608

143.97666

1.8149399

1.1100825

-1.847072

0.03731102

367.2949

6.140707

0.4165181

0

175.11513

94,299

169,053

263,840

you

type

p < .001***

950

851

10.0341160

5.03065091

214.0251

0.0050034651

1.994596

0.0000050034651

0.3321303

731.9492

98.95010

66.42605

0.6954194

0.5557786

-2.401376

0.02914781

224.1572

2.004550

0.1698013

0

646.27531

93,727

168,312

263,840

could

type

p < .001***

378

211

3.9925219

1.24731768

194.3144

0.0027452043

3.200880

0.0000027452043

0.5239100

439.4929

164.70635

104.78200

1.1651328

0.8386960

-2.118459

0.02790018

205.3783

3.206349

0.2835562

0

211.35822

94,299

168,952

263,840

telescreen

type

p < .001***

90

0

0.9506005

0.00000000

184.5139

0.0009506005

160,808.212797

0.0000009506005

1.0000000

184.4769

62.38325

200.00000

5.7798374

1.4785774

-1.478577

0.02469195

160.8613

323.706552

0.6413763

0

32.29582

94,587

169,163

263,840

The above table shows the keywords for text1, i.e. for George Orwell’s 1984.

Visualising keywords

Dotplots

We can now visualize the keyness strengths in a dotplot as shown in the code chunk below.

# sort the assoc_tb3 data frame in descending order based on the 'G2' column
assoc_tb3 %>%
  dplyr::arrange(-G2) %>%
  # select the top 20 rows after sorting
  head(20) %>%
  # create a ggplot with 'token' on the x-axis (reordered by 'G2') and 'G2' on the y-axis
  ggplot(aes(x = reorder(token, G2, mean), y = G2)) +
  # add a scatter plot with points representing the 'G2' values
  geom_point() +
  # flip the coordinates to have horizontal points
  coord_flip() +
  # set the theme to a basic white and black theme
  theme_bw() +
  # set the x-axis label to "Token" and y-axis label to "Keyness (G2)"
  labs(x = "Token", y = "Keyness (G2)")

Barplots

Another option to visualize keyness is a barplot as shown below.

# get top 10 keywords for text 1
top <- assoc_tb3 %>% dplyr::ungroup() %>% dplyr::slice_head(n = 12)
# get top 10 keywords for text 2
bot <- assoc_tb3 %>% dplyr::ungroup() %>% dplyr::slice_tail(n = 12)
# combine into table
rbind(top, bot) %>%
  # create a ggplot
  ggplot(aes(x = reorder(token, G2, mean), y = G2, label = G2, fill = type)) +
  # add a bar plot using the 'phi' values
  geom_bar(stat = "identity") +
  # add text labels above the bars with rounded 'phi' values
  geom_text(aes(y = ifelse(G2> 0, G2 - 50, G2 + 50), 
                label = round(G2, 1)), color = "white", size = 3) + 
  # flip the coordinates to have horizontal bars
  coord_flip() +
  # set the theme to a basic white and black theme
  theme_bw() +
  # remove legend
  theme(legend.position = "none") +
    # define colors
  scale_fill_manual(values = c("orange","darkgray")) +
  # set the x-axis label to "Token" and y-axis label to "Keyness (G2)"
  labs(title = "Top 10 keywords for text1 and text 2", x = "Keyword", y = "Keyness (G2)")

Comparative wordclouds

Another form of word clouds, known as comparison clouds, is helpful in discerning disparities between texts. The problem compared to previous, more informative methods for identifying keywords is that comparison clouds use a very basic and not very sophisticated methods for identifying keywords. Nonetheless, comparison clouds are very useful visualization tools during initial steps on an analysis.

In a first step, we generate a corpus object from the texts and create a variable with the author name.

corp_dom <- quanteda::corpus(c(text1, text2)) 
attr(corp_dom, "docvars")$Author = c("Orwell", "Melville")

Now, we can remove so-called stopwords (non-lexical function words) and punctuation and generate the comparison cloud.

# create a comparison word cloud for a corpus
corp_dom %>%
  # tokenize the corpus, removing punctuation, symbols, and numbers
  quanteda::tokens(remove_punct = TRUE,
                   remove_symbols = TRUE,
                   remove_numbers = TRUE) %>%
  # remove English stopwords
  quanteda::tokens_remove(stopwords("english")) %>%
  # create a Document-Feature Matrix (DFM)
  quanteda::dfm() %>%
  # group the DFM by the 'Author' column from 'corp_dom'
  quanteda::dfm_group(groups = corp_dom$Author) %>%
  # trim the DFM, keeping terms that occur at least 10 times
  quanteda::dfm_trim(min_termfreq = 10, verbose = FALSE) %>%
  # generate a comparison word cloud
  quanteda.textplots::textplot_wordcloud(
    # create a comparison word cloud
    comparison = TRUE,  
    # set colors for different groups
    color = c("darkgray", "orange"),  
    # define the maximum number of words to display in the word cloud
    max_words = 150)  

Citation & Session Info

Schweinberger, Martin. 2024. Keyness and Keyword Analysis in R. Brisbane: The University of Queensland. url: https://ladal.edu.au/coll.html (Version 2024.03.28).

@manual{schweinberger`2024key,
  author = {Schweinberger, Martin},
  title = {Keyness and Keyword Analysis in R},
  note = {https://ladal.edu.au/key.html},
  year = {2024},
  organization = {The University of Queensland, Australia. School of Languages and Cultures},
  address = {Brisbane},
  edition = {2024.03.28}
}
sessionInfo()
## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22621)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_Australia.utf8  LC_CTYPE=English_Australia.utf8   
## [3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C                      
## [5] LC_TIME=English_Australia.utf8    
## 
## time zone: Australia/Brisbane
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.5.0             sna_2.7-2                
##  [3] network_1.18.2            statnet.common_4.9.0     
##  [5] tm_0.7-11                 NLP_0.2-1                
##  [7] stringr_1.5.1             dplyr_1.1.4              
##  [9] quanteda.textplots_0.94.4 quanteda.textstats_0.96.4
## [11] quanteda_3.3.1            Matrix_1.6-5             
## [13] flextable_0.9.4          
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1        farver_2.1.1            fastmap_1.1.1          
##  [4] fontquiver_0.2.1        promises_1.2.1          digest_0.6.34          
##  [7] mime_0.12               lifecycle_1.0.4         ellipsis_0.3.2         
## [10] gfonts_0.2.0            magrittr_2.0.3          compiler_4.3.2         
## [13] rlang_1.1.3             sass_0.4.8              tools_4.3.2            
## [16] utf8_1.2.4              yaml_2.3.8              data.table_1.15.2      
## [19] knitr_1.45              askpass_1.2.0           labeling_0.4.3         
## [22] stopwords_2.3           curl_5.2.0              xml2_1.3.6             
## [25] httpcode_0.3.0          klippy_0.0.0.9500       withr_3.0.0            
## [28] purrr_1.0.2             grid_4.3.2              fansi_1.0.6            
## [31] gdtools_0.3.6           xtable_1.8-4            colorspace_2.1-0       
## [34] scales_1.3.0            crul_1.4.0              cli_3.6.2              
## [37] rmarkdown_2.25          crayon_1.5.2            ragg_1.2.7             
## [40] generics_0.1.3          RcppParallel_5.1.7      rstudioapi_0.15.0      
## [43] cachem_1.0.8            assertthat_0.2.1        parallel_4.3.2         
## [46] vctrs_0.6.5             jsonlite_1.8.8          slam_0.1-50            
## [49] fontBitstreamVera_0.1.1 systemfonts_1.0.5       jquerylib_0.1.4        
## [52] tidyr_1.3.1             glue_1.7.0              stringi_1.8.3          
## [55] gtable_0.3.4            later_1.3.2             munsell_0.5.0          
## [58] tibble_3.2.1            pillar_1.9.0            htmltools_0.5.7        
## [61] openssl_2.1.1           R6_2.5.1                textshaping_0.3.7      
## [64] evaluate_0.23           shiny_1.8.0             lattice_0.21-9         
## [67] highr_0.10              fontLiberation_0.1.0    httpuv_1.6.14          
## [70] bslib_0.6.1             Rcpp_1.0.12             zip_2.3.1              
## [73] uuid_1.2-0              fastmatch_1.1-4         nsyllable_1.0.1        
## [76] coda_0.19-4.1           officer_0.6.5           xfun_0.42              
## [79] pkgconfig_2.0.3

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References

Egbert, Jesse, and Douglas Biber. 2019. “Incorporating Text Dispersion into Keyword Analysis.” Corpora 14 (1): 77–104. https://doi.org/10.3366/cor.2019.0162.
Sönning, Lukas. 2023. Corpus Linguistics and Linguistic Theory. https://doi.org/doi:10.1515/cllt-2022-0116.

  1. If you want to render the R Notebook on your machine, i.e. knitting the document to html or a pdf, you need to make sure that you have R and RStudio installed and you also need to download the bibliography file and store it in the same folder where you store the Rmd file.↩︎