Introduction to Text Analysis: Practical Implementation in R

Introduction

This is Part 2 of the LADAL Introduction to Text Analysis tutorial series. Where Part 1 focused on concepts and theoretical foundations, this tutorial focuses on practical implementation: how to apply selected text analysis methods in R using real corpus data. Each section introduces a method, explains the key decisions involved, demonstrates the code, and provides exercises to consolidate understanding.

The methods covered — concordancing, word frequency analysis, collocation analysis, and keyword analysis — represent the core quantitative techniques of corpus-based text analysis. They are foundational: most more advanced analyses (topic modelling, stylometry, sentiment analysis) build upon or assume familiarity with these basic tools.

Prerequisite Tutorials

Please complete these before working through this tutorial:

• Introduction to Text Analysis — Part 1: Concepts and Foundations
  
• Getting Started with R and RStudio
  
• Loading, Saving, and Generating Data in R
  
• String Processing in R
  
• Regular Expressions in R
  

What This Tutorial Covers

1. Setup — packages, data, and the quanteda workflow
   
2. Concordancing — KWIC searches, regex patterns, phrase searches
   
3. Word Frequency Analysis — frequency lists, stopwords, visualisation, dispersion
   
4. Collocation Analysis — co-occurrence matrices, association measures, network plots
   
5. Keyword Analysis — comparing corpora, keyness metrics, interpretation
   
6. Quick Reference — essential functions and workflow summary
   

Citation

Martin Schweinberger. 2026. Introduction to Text Analysis: Practical Implementation in R. The Language Technology and Data Analysis Laboratory (LADAL), The University of Queensland, Australia. url: https://ladal.edu.au/tutorials/textanalysis/textanalysis.html (Version 2026.03.28), doi: .

Setup and Data

Section Overview

What you’ll learn: How to install and load the required packages, and how the tutorial data is structured

Installing Packages

# Core text analysis framework  
install.packages("quanteda")  
install.packages("quanteda.textplots")  
install.packages("quanteda.textstats")  
  
# Data manipulation and visualisation  
install.packages("dplyr")  
install.packages("tidyr")  
install.packages("stringr")  
install.packages("ggplot2")  
  
# Text utilities  
install.packages("tidytext")  
install.packages("tokenizers")  
install.packages("flextable")  
install.packages("here")  
install.packages("checkdown")  

Loading Packages

library(quanteda)               # core text analysis framework  
library(quanteda.textplots)     # visualisation (word clouds, networks)  
library(quanteda.textstats)     # statistical measures  
library(dplyr)                  # data wrangling  
library(tidyr)                  # data reshaping  
library(stringr)                # string processing  
library(ggplot2)                # plotting  
library(tidytext)               # tidy text mining  
library(tokenizers)             # sentence tokenisation  
library(flextable)              # formatted tables  
library(here)                   # portable file paths  
library(checkdown)              # interactive exercises 
library(quanteda.textstats)     # for frequency analysis

Loading All Packages at the Top

Always load all required packages in a single code chunk at the top of your script. This makes dependencies immediately visible, ensures reproducibility (anyone running the script knows exactly what they need), and makes troubleshooting easier when a package is missing or out of date.

Tutorial Data

This tutorial uses Lewis Carroll’s Alice’s Adventures in Wonderland (1865), sourced from Project Gutenberg. The text is in the public domain and provides a convenient single-author literary corpus with recognisable content.

# Load Alice's Adventures in Wonderland from Project Gutenberg  
# Using the gutenbergr package or pre-saved data  
# For this tutorial we load a pre-saved plain text version  
alice_path <- here::here("tutorials", "textanalysis", "data", "alice.rda")  
  
# Load the data (a character vector, one element per line)  
if (file.exists(alice_path)) {  
  alice_raw <- readRDS(alice_path)  
} else {  
  # If file not present, download directly  
  if (!requireNamespace("gutenbergr", quietly = TRUE)) {  
    install.packages("gutenbergr")  
  }  
  alice_raw <- gutenbergr::gutenberg_download(11)$text  
}  
  
# Quick inspection  
length(alice_raw)  

[1] 2479

head(alice_raw, 8)  

[1] "Alice’s Adventures in Wonderland"                                     
[2] "by Lewis Carroll"                                                     
[3] "CHAPTER I."                                                           
[4] "Down the Rabbit-Hole"                                                 
[5] "Alice was beginning to get very tired of sitting by her sister on the"
[6] "bank, and of having nothing to do: once or twice she had peeped into" 
[7] "the book her sister was reading, but it had no pictures or"           
[8] "conversations in it, “and what is the use of a book,” thought Alice"  

Preparing the Text: Splitting into Chapters

For most analyses it is useful to work with the text segmented into meaningful units. We split Alice into chapters, which will serve as our “documents” in document-level analyses:

# Combine all lines, mark chapter boundaries, split into chapters  
alice_chapters <- alice_raw |>  
  # Collapse to single string  
  paste0(collapse = " ") |>  
  # Insert a unique marker before each chapter heading  
  # The regex matches: "CHAPTER" + roman numerals (1-7 chars) + optional period  
  stringr::str_replace_all("(CHAPTER [XVI]{1,7}\\.{0,1}) ", "|||\\1 ") |>  
  # Lowercase the whole text  
  tolower() |>  
  # Split on the chapter boundary marker  
  stringr::str_split("\\|\\|\\|") |>  
  unlist() |>  
  # Remove any empty strings  
  (\(x) x[nzchar(stringr::str_trim(x))])()  
  
# How many segments?  
cat("Number of segments:", length(alice_chapters), "\n")  

Number of segments: 13 

cat("Chapter 2 begins:", substr(alice_chapters[2], 1, 60), "...\n")  

Chapter 2 begins: chapter i. down the rabbit-hole alice was beginning to get v ...

Regex Explained: (CHAPTER [XVI]{1,7}\\.{0,1})

• CHAPTER — matches the literal word “CHAPTER”
  
• [XVI]{1,7} — matches 1 to 7 Roman numeral characters (I, V, X, L)
  
• \\.{0,1} — matches an optional period (the \\. escapes the dot; {0,1} makes it optional)

The whole group is wrapped in (...) for capture. We insert ||| before each match to create a unique split point. The {0,1} is equivalent to ? — using the explicit form makes the intent clearer.

The quanteda Workflow

The quanteda package provides a consistent workflow for corpus-based text analysis built around three core objects:

Object	Created by	Contains	Used for
corpus	corpus()	Document collection with metadata	Storage, metadata management, subsetting
tokens	tokens()	Tokenised text — a list of character vectors	Concordancing, collocations, n-grams
dfm	dfm()	Document-Feature Matrix: documents × word frequencies	Frequency analysis, keywords, topic modelling, classification

# Build the three core objects  
alice_corpus <- quanteda::corpus(alice_chapters)  
  
alice_tokens <- quanteda::tokens(alice_corpus,  
                                 remove_punct  = FALSE,  
                                 remove_symbols = TRUE,  
                                 remove_numbers = FALSE)  
  
alice_dfm <- alice_tokens |>  
  quanteda::tokens_remove(quanteda::stopwords("english")) |>  
  quanteda::dfm()  
  
cat("Corpus: ", ndoc(alice_corpus), "documents\n")  

Corpus:  13 documents

cat("Tokens: ", sum(ntoken(alice_tokens)), "total tokens\n")  

Tokens:  33571 total tokens

cat("DFM:    ", nfeat(alice_dfm), "unique features (after stopword removal)\n")  

DFM:     2753 unique features (after stopword removal)

---

Concordancing

Section Overview

What you’ll learn: How to extract KWIC (Key Word In Context) displays using quanteda::kwic(), search with regular expressions, and search for multi-word phrases

Key function: quanteda::kwic()

When to use it: At the start of any analysis involving a specific word or phrase; to inspect how terms are used; to extract authentic examples; to identify patterns that quantitative measures cannot reveal

Concordancing is the foundation of corpus-based text analysis. Before drawing quantitative conclusions about how a word behaves, it is essential to look at actual examples and understand the range of contexts in which the word appears. The kwic() function in quanteda produces KWIC displays directly:

Simple Word Search

# Concordance for "alice" — 5-word window on each side  
kwic_alice <- quanteda::kwic(  
  x       = alice_tokens,  
  pattern = "alice",  
  window  = 5  
) |>  
  as.data.frame() |>  
  dplyr::select(docname, pre, keyword, post)  
  
# How many occurrences?  
cat("Occurrences of 'alice':", nrow(kwic_alice), "\n")  

Occurrences of 'alice': 386 

docname	pre	keyword	post
text2	i . down the rabbit-hole	alice	was beginning to get very
text2	a book , ” thought	alice	“ without pictures or conversations
text2	in that ; nor did	alice	think it so _very_ much
text2	and then hurried on ,	alice	started to her feet ,
text2	in another moment down went	alice	after it , never once
text2	down , so suddenly that	alice	had not a moment to
text2	“ well ! ” thought	alice	to herself , “ after
text2	for , you see ,	alice	had learnt several things of

The docname column shows which chapter (document) the occurrence is from. pre is the left context, keyword is the search term, and post is the right context.

Pattern Matching with Regular Expressions

# Find all word forms beginning with "walk"  
kwic_walk <- quanteda::kwic(  
  x         = alice_tokens,  
  pattern   = "walk.*",  
  window    = 5,  
  valuetype = "regex"   # enable regular expression matching  
) |>  
  as.data.frame() |>  
  dplyr::select(docname, pre, keyword, post)  
  
cat("Forms of 'walk':", nrow(kwic_walk), "\n")  

Forms of 'walk': 20 

# What distinct forms were found?  
unique(kwic_walk$keyword)  

[1] "walk"    "walking" "walked" 

docname	pre	keyword	post
text2	out among the people that	walk	with their heads downward !
text2	to dream that she was	walking	hand in hand with dinah
text2	trying every door , she	walked	sadly down the middle ,
text3	“ or perhaps they won’t	walk	the way i want to
text4	mouse , getting up and	walking	away . “ you insult
text4	its head impatiently , and	walked	a little quicker . “
text5	and get ready for your	walk	! ’ ‘ coming in
text7	, “ if you only	walk	long enough . ” alice

Three valuetype Options in kwic()

• "glob" (default): simple wildcards — * matches any sequence, ? matches any single character. E.g., "walk*" matches walk, walks, walked
  
• "regex": full regular expression syntax — "walk.*" matches the same
  
• "fixed": exact string match — "walk" matches only walk, not walks

For corpus searches, "regex" is the most powerful but also requires care with escaping special characters.

Phrase Search

# Search for the multi-word phrase "poor alice"  
kwic_pooralice <- quanteda::kwic(  
  x       = alice_tokens,  
  pattern = quanteda::phrase("poor alice"),   # phrase() enables multi-word search  
  window  = 6  
) |>  
  as.data.frame() |>  
  dplyr::select(docname, pre, keyword, post)  
  
cat("Occurrences of 'poor alice':", nrow(kwic_pooralice), "\n")  

Occurrences of 'poor alice': 11 

docname	pre	keyword	post
text2	would go through , ” thought	poor alice	, “ it would be of
text2	once ; but , alas for	poor alice	! when she got to the
text2	no use now , ” thought	poor alice	, “ to pretend to be
text3	off to the garden door .	poor alice	! it was as much as
text3	the right words , ” said	poor alice	, and her eyes filled with
text4	didn’t mean it ! ” pleaded	poor alice	. “ but you’re so easily
text4	any more ! ” and here	poor alice	began to cry again , for
text5	pleasanter at home , ” thought	poor alice	, “ when one wasn’t always
text6	used to it ! ” pleaded	poor alice	in a piteous tone . and
text8	. ” this answer so confused	poor alice	, that she let the dormouse
text10	both sat silent and looked at	poor alice	, who felt ready to sink

When to Use phrase() vs. a Plain Pattern

Without phrase(), a pattern like "poor alice" would be interpreted as two separate patterns. phrase() tells quanteda to look for the words adjacent to each other as a sequence. Use phrase() for:

• Multi-word expressions and idioms (by and large, make a decision)
  
• Named entities (Mad Hatter, White Rabbit)
  
• Fixed collocations where word order matters
  

Sorting Concordances for Pattern Analysis

Sorting KWIC output by the left or right context makes patterns more visible:

# Sort by one word to the right of the keyword (R1)  
# This groups concordances by what immediately follows "said"  
kwic_said <- quanteda::kwic(  
  x       = alice_tokens,  
  pattern = "said",  
  window  = 4  
) |>  
  as.data.frame() |>  
  dplyr::select(docname, pre, keyword, post) |>  
  # Extract the first word of the right context  
  dplyr::mutate(R1 = stringr::word(post, 1)) |>  
  # Sort by R1 to see what typically follows "said"  
  dplyr::arrange(R1)  
  
# Most common words following "said"  
kwic_said |>  
  dplyr::count(R1, sort = TRUE) |>  
  head(10)  

        R1   n
1      the 207
2    alice 115
3       to  39
4        ,  28
5       in   7
6  nothing   6
7     this   6
8        “   5
9        .   4
10       :   3

R1	n
the	207
alice	115
to	39
,	28
in	7
nothing	6
this	6
“	5
.	4
:	3

Sorting reveals a pattern immediately: “said” is most often followed by the character names (alice, the) — the classic dialogue attribution pattern “…” said Alice.

---

Exercises: Concordancing

Q1. What does valuetype = "regex" do in kwic(), and why would you use it instead of the default "glob"?

It makes the search case-insensitive
It searches the entire corpus rather than individual documents
It enables full regular expression syntax, giving access to anchors (^, $), character classes ([aeiou]), quantifiers ({n,m}), and other regex constructs that glob wildcards cannot express
It returns only exact matches, with no wildcards

---

Q2. You want to find all occurrences of run, runs, ran, and running in a corpus. Which pattern and valuetype combination would work correctly?

"run" with valuetype = "fixed" — fixed matching finds all forms
"run*" with valuetype = "glob" — the * matches any suffix
"run.*" with valuetype = "regex" — .* matches any suffix including s, ning, etc.
"\\brun(s|ning)?\\b|\\bran\\b" with valuetype = "regex" — covers all four forms using alternation

---

Word Frequency Analysis

Section Overview

What you’ll learn: How to build word frequency lists, handle stopwords, visualise frequency distributions, and track word frequency across document sections

Key functions: quanteda::dfm(), quanteda::textstat_frequency(), tidytext::stop_words, ggplot2

When to use it: As one of the first steps in any corpus analysis; for vocabulary profiling; for identifying dominant themes; for comparing texts

Word frequency analysis counts how often each word occurs in a text or corpus. Despite its simplicity, it is one of the most informative first steps in text analysis — the vocabulary profile of a text reveals its themes, register, and style.

Building a Frequency List

# Tokenise Alice, remove punctuation
alice_tokens_clean <- quanteda::tokens(
  quanteda::corpus(paste(alice_raw, collapse = " ")),
  remove_punct   = TRUE,
  remove_symbols = TRUE,
  remove_numbers = TRUE
)

# Create a DFM (no stopword removal yet)
alice_dfm_all <- quanteda::dfm(alice_tokens_clean)

# Extract frequency table using quanteda.textstats
freq_all <- quanteda.textstats::textstat_frequency(alice_dfm_all) |>
  as.data.frame() |>
  dplyr::select(feature, frequency, rank)

cat("Total tokens:", sum(freq_all$frequency), "\n")

Total tokens: 26422 

cat("Unique types:", nrow(freq_all), "\n")

Unique types: 2858 

cat("Type-token ratio:", round(nrow(freq_all) / sum(freq_all$frequency), 3), "\n")

Type-token ratio: 0.108 

feature	frequency	freq_rank
the	1,631	1
and	845	2
to	721	3
a	627	4
she	536	5
it	525	6
of	508	7
said	460	8
i	386	9
alice	386	9
in	365	11
was	352	12
you	347	13
that	266	14
as	262	15

The top of the frequency list is dominated by function words (the, and, to, a, of, it, she, was…) — grammatically necessary but semantically light. This is typical of English text and reflects Zipf’s Law: a small number of very frequent words account for a disproportionate share of all tokens.

Removing Stopwords

Stopwords are high-frequency function words that are removed before frequency analysis when the goal is to identify content-bearing vocabulary. The appropriate stopword list depends on the language and the research question:

# Remove English stopwords
alice_dfm_nostop <- alice_tokens_clean |>
  quanteda::tokens_remove(quanteda::stopwords("english")) |>
  quanteda::dfm()

freq_nostop <- quanteda.textstats::textstat_frequency(alice_dfm_nostop) |>
  as.data.frame() |>
  dplyr::select(feature, frequency, rank) 

feature	frequency	rank
said	460	1
alice	386	2
little	126	3
one	98	4
like	85	5
know	85	5
went	83	7
thought	74	8
time	68	9
queen	68	9
see	66	11
king	61	12
well	60	13
don’t	59	14
now	58	15

Now the list shows content words that reflect the novel’s themes: alice, said, queen, time, little, thought, king. The character alice is the most frequent content word — unsurprisingly, since the novel is named after her.

Should You Always Remove Stopwords?

Stopword removal is appropriate when analysing content (what the text is about). It is not appropriate when:

• Analysing style (function word frequencies are strong style markers used in authorship attribution)
  
• Studying discourse (function words like but, although, however carry logical structure)
  
• Building language models (function words are necessary for grammar)
  
• Performing concordancing (you usually want to see full context)

Always ask: does removing these words serve my research question?

Visualising Frequency

Bar Plot

freq_nostop |>  
  head(15) |>  
  ggplot(aes(x = reorder(feature, frequency), y = frequency)) +  
  geom_col(fill = "steelblue", width = 0.75) +  
  coord_flip() +  
  labs(  
    title    = "15 Most Frequent Content Words",  
    subtitle = "Alice's Adventures in Wonderland (stopwords removed)",  
    x        = NULL,  
    y        = "Frequency"  
  ) +  
  theme_bw() +  
  theme(panel.grid.minor = element_blank())  

Word Cloud

alice_dfm_nostop |>  
  quanteda::dfm_trim(min_termfreq = 5) |>  
  quanteda.textplots::textplot_wordcloud(  
    max_words = 120,  
    max_size  = 8,  
    min_size  = 1.5,  
    color     = c("steelblue", "tomato", "seagreen", "orange", "purple")  
  )  

Word Clouds: Use with Caution

Word clouds are visually appealing and useful for exploratory work or communicating with non-specialist audiences. However, they have serious limitations as analytical tools:

• It is difficult to compare sizes precisely — the visual ranking is approximate
  
• Layout is partly random — words in the centre are not more important
  
• They convey no information about distribution across documents
  
• They can create misleading impressions about relative frequency

For rigorous analysis, always use bar plots or tables rather than word clouds.

Comparison Across Authors

One of the most powerful uses of frequency analysis is comparing vocabulary across different texts or authors:

# load three texts  
darwin <- readRDS(here::here("tutorials/textanalysis/data", "darwin.rda")) |>
    paste(collapse = " ")
melville <- readRDS(here::here("tutorials/textanalysis/data", "melville.rda")) |>
    paste(collapse = " ")
orwell <- readRDS(here::here("tutorials/textanalysis/data", "orwell.rda")) |>
    paste(collapse = " ")
# Create multi-document corpus
comp_texts  <- c(darwin, melville, orwell)
comp_corpus <- quanteda::corpus(
  comp_texts,
  docnames = c("Darwin", "Melville", "Orwell")
  )

# Comparison word cloud — shows distinctive vocabulary per text  
# Create DFM for comparison
comp_dfm <- comp_corpus |>
  quanteda::tokens(
    remove_punct   = TRUE,
    remove_numbers = TRUE
  ) |>
  quanteda::tokens_tolower() |>
  quanteda::tokens_remove(quanteda::stopwords("english")) |>
  quanteda::tokens_wordstem() |>
  quanteda::dfm() |>
  quanteda::dfm_trim(min_docfreq = 2)

# Only plot if features remain
quanteda.textplots::textplot_wordcloud(
    comp_dfm,
    comparison = TRUE,
    max_words  = 100,
    color      = c("steelblue", "tomato", "seagreen")
  )

Dispersion: Frequency Across Document Sections

Tracking how word frequency changes across sections reveals narrative or structural patterns:

# Count words and target word occurrences per chapter  
dispersion_tb <- data.frame(  
  chapter  = seq_along(alice_chapters),  
  n_words  = stringr::str_count(alice_chapters, "\\S+"),  
  n_alice  = stringr::str_count(alice_chapters, "\\balice\\b"),  
  n_queen  = stringr::str_count(alice_chapters, "\\bqueen\\b")  
) |>  
  dplyr::mutate(  
    # Relative frequency per 1,000 words  
    alice_per1k = round(n_alice / n_words * 1000, 2),  
    queen_per1k = round(n_queen / n_words * 1000, 2),  
    chapter_f   = factor(chapter, levels = chapter)  
  ) |>  
  dplyr::filter(n_words > 50)  # exclude very short intro segments  
  
# Reshape for plotting  
dispersion_long <- dispersion_tb |>  
  tidyr::pivot_longer(  
    cols      = c(alice_per1k, queen_per1k),  
    names_to  = "word",  
    values_to = "freq_per1k"  
  ) |>  
  dplyr::mutate(  
    word = dplyr::recode(word,  
                         "alice_per1k" = "alice",  
                         "queen_per1k" = "queen")  
  )  
  
ggplot(dispersion_long,  
       aes(x = chapter_f, y = freq_per1k,  
           colour = word, group = word)) +  
  geom_line(linewidth = 0.8) +  
  geom_point(size = 2.5) +  
  scale_colour_manual(values = c("alice" = "steelblue", "queen" = "tomato")) +  
  labs(  
    title    = "Word Frequency Across Chapters",  
    subtitle = "Relative frequency per 1,000 words",  
    x        = "Chapter",  
    y        = "Frequency per 1,000 words",  
    colour   = "Word"  
  ) +  
  theme_bw() +  
  theme(panel.grid.minor  = element_blank(),  
        axis.text.x       = element_text(angle = 45, hjust = 1))  

This dispersion plot reveals narrative structure: alice is consistently mentioned throughout, while queen appears mainly from mid-text onwards (reflecting the plot — the Queen of Hearts is introduced later in the story).

---

Exercises: Frequency Analysis

Q1. You build a frequency list from a news corpus and find that the is the most frequent word (frequency: 84,231). A colleague says this is important evidence that the corpus is primarily about the economy. What is wrong with this reasoning?

84,231 is not enough occurrences to draw conclusions
Frequency lists should only contain nouns and verbs
The colleague is correct — 'the' is indeed the most important word
'the' is a function word (definite article) that appears frequently in all English texts regardless of topic; its frequency reflects English grammar, not the content of the corpus

---

Q2. What does a dispersion plot show that a simple total frequency count does not?

Whether the word is a stopword or content word
The total number of unique types in the corpus
How word frequency varies across different sections or documents — whether a word appears throughout a text or is concentrated in specific parts
The statistical significance of the word's frequency

---

Collocation Analysis

Section Overview

What you’ll learn: How to extract and quantify collocations — words that co-occur more than expected by chance — using co-occurrence matrices and association measures

Key concepts: Contingency table, Mutual Information (MI), phi (φ), Delta P (ΔP)

When to use it: When studying phraseological patterns, lexical associations, semantic prosody, or the characteristic usage of a target word

Collocations reveal the hidden patterning of language: every word has characteristic co-occurrence partners, and these partnerships are stable, culturally embedded, and often non-compositional. Strong coffee is a collocation; powerful coffee is not — even though strong and powerful are near-synonyms. Detecting collocations computationally requires statistical comparison: is this word pair’s co-occurrence frequency higher than we would expect if the two words were distributed independently?

The Contingency Table Approach

Collocation strength is measured using a 2×2 contingency table that cross-tabulates the presence and absence of two words:

	w₂ present	w₂ absent
w₁ present	O₁₁	O₁₂	= R₁
w₁ absent	O₂₁	O₂₂	= R₂
	= C₁	= C₂	= N

Where O₁₁ = both words co-occur in the same context window, O₁₂ = w₁ appears but not w₂, O₂₁ = w₂ appears but not w₁, and O₂₂ = neither appears. N is the total number of co-occurrence opportunities (contexts).

From observed (O) counts we compute expected (E) counts under the assumption of independence: E₁₁ = R₁ × C₁ / N. Association measures compare O₁₁ to E₁₁: if O₁₁ >> E₁₁, the words co-occur more than chance and are likely collocates.

Preparing Sentence-Level Data

For collocation analysis, we compute co-occurrences within sentences (rather than the whole text), so that bank and interest are counted as co-occurring only when they appear in the same sentence:

# Tokenise Alice into sentences, then clean  
alice_sentences <- paste(alice_raw, collapse = " ") |>  
  tokenizers::tokenize_sentences() |>  
  unlist() |>  
  stringr::str_replace_all("[^[:alnum:] ]", " ") |>  
  stringr::str_squish() |>  
  tolower() |>  
  (\(x) x[nchar(x) > 5])()   # remove very short fragments  
  
cat("Number of sentences:", length(alice_sentences), "\n")  

Number of sentences: 1585 

Building the Co-occurrence Matrix

# Create a Feature Co-occurrence Matrix (FCM)  
# FCM counts how often any two words appear in the same sentence  
coll_fcm <- alice_sentences |>  
  quanteda::tokens() |>  
  quanteda::dfm() |>  
  quanteda::fcm(tri = FALSE)   # tri=FALSE: count both (w1,w2) and (w2,w1)  
  
# Convert to long-format data frame  
coll_basic <- tidytext::tidy(coll_fcm) |>  
  dplyr::rename(w1 = term, w2 = document, O11 = count)  
  
cat("Unique word pairs:", nrow(coll_basic), "\n")  

Unique word pairs: 274011 

Computing Association Measures

# Compute the full contingency table and association measures  
coll_stats <- coll_basic |>  
  dplyr::mutate(N = sum(O11)) |>  
  dplyr::group_by(w1) |>  
  dplyr::mutate(R1 = sum(O11), O12 = R1 - O11, R2 = N - R1) |>  
  dplyr::ungroup() |>  
  dplyr::group_by(w2) |>  
  dplyr::mutate(C1 = sum(O11), O21 = C1 - O11, C2 = N - C1,  
                O22 = R2 - O21) |>  
  dplyr::ungroup() |>  
  dplyr::mutate(  
    # Expected co-occurrence under independence  
    E11 = R1 * C1 / N,  
    E12 = R1 * C2 / N,  
    E21 = R2 * C1 / N,  
    E22 = R2 * C2 / N,  
    # Association measures  
    MI      = log2(O11 / E11),                        # Mutual Information  
    X2      = (O11 - E11)^2/E11 + (O12 - E12)^2/E12 +  
               (O21 - E21)^2/E21 + (O22 - E22)^2/E22,  # Chi-square  
    phi     = sqrt(X2 / N),                            # Effect size (phi)  
    DeltaP  = (O11/(O11 + O12)) - (O21/(O21 + O22))  # Directional measure  
  )  

Collocates of a Target Word

# Find collocates of "alice"  
alice_colls <- coll_stats |>  
  dplyr::filter(  
    w1 == "alice",  
    (O11 + O21) >= 10,  # w2 must occur at least 10 times in the corpus  
    O11 >= 5            # must co-occur at least 5 times with alice  
  ) |>  
  # Bonferroni-corrected significance filter via chi-square  
  dplyr::filter(X2 > qchisq(0.99, 1)) |>  
  # Keep only attraction collocates (observed > expected)  
  dplyr::filter(O11 > E11) |>  
  dplyr::arrange(dplyr::desc(phi))  

w1	w2	O11	E11	phi	MI	DeltaP
alice	said	179	87.539	0.010	1.032	0.010
alice	thought	60	22.086	0.009	1.442	0.004
alice	very	86	53.588	0.005	0.682	0.003
alice	turning	7	1.622	0.005	2.110	0.001
alice	replied	14	5.127	0.004	1.449	0.001
alice	afraid	10	3.200	0.004	1.644	0.001
alice	asked	5	1.176	0.004	2.089	0.000
alice	to	334	276.236	0.004	0.274	0.006
alice	i	163	128.033	0.003	0.348	0.004
alice	politely	5	1.404	0.003	1.832	0.000
alice	cried	10	3.962	0.003	1.336	0.001
alice	much	32	19.441	0.003	0.719	0.001

Interpreting the Association Measures

• O11: Raw co-occurrence count — how many times the pair actually appeared together
  
• E11: Expected count under independence — how often they would co-occur if word choice were random
  
• phi (φ): Effect size, 0–1 scale. Higher = stronger association. Preferred for binary data.
  
• MI (Mutual Information): Measures how much information one word provides about the other. MI > 0 means positive association; MI >> 0 means strong association. Biased toward rare words.
  
• DeltaP: Directional measure — how much does seeing w1 increase the probability of seeing w2? A positive value means w2 is more likely given w1.

For a corpus linguistics audience, phi and Delta P are generally the most interpretable measures. MI is commonly used in computational linguistics but can inflate the importance of rare words.

Visualising Collocations as a Network

library(ggraph)  
library(igraph)  
  
# Get top 20 collocates and build a co-occurrence matrix for them  
top_colls <- alice_colls |>  
  dplyr::arrange(dplyr::desc(phi)) |>  
  head(20) |>  
  dplyr::pull(w2)  
  
# Build FCM restricted to alice + its top collocates  
coll_network_data <- alice_sentences |>  
  quanteda::tokens() |>  
  quanteda::dfm() |>  
  quanteda::dfm_select(pattern = c("alice", top_colls)) |>  
  quanteda::fcm(tri = FALSE)  
  
# Convert to igraph for ggraph  
net <- igraph::graph_from_adjacency_matrix(  
  as.matrix(coll_network_data),  
  mode    = "undirected",  
  weighted = TRUE,  
  diag    = FALSE  
)  
  
# Plot  
ggraph::ggraph(net, layout = "fr") +  
  ggraph::geom_edge_link(aes(width = weight, alpha = weight),  
                         colour = "steelblue") +  
  ggraph::geom_node_point(colour = "tomato", size = 5) +  
  ggraph::geom_node_text(aes(label = name), repel = TRUE, size = 3.5) +  
  ggraph::scale_edge_width(range = c(0.3, 2.5)) +  
  ggraph::scale_edge_alpha(range = c(0.3, 0.9)) +  
  labs(  
    title    = "Collocation Network for 'alice'",  
    subtitle = "Edge weight = co-occurrence frequency; top 20 collocates"  
  ) +  
  theme_graph() +  
  theme(legend.position = "none")  

The network plot reveals the semantic neighbourhood of alice in the novel: her characteristic actions (said, thought, went), the characters she interacts with (queen, king, cat, rabbit), and the emotions associated with her (poor).

---

Exercises: Collocation Analysis

Q1. What does it mean when O11 >> E11 in a collocation analysis?

The co-occurrence frequency is statistically non-significant
The two words are synonyms and can be substituted for each other
The words always appear next to each other in fixed order
The two words co-occur much more often than expected if their distributions were independent — they are attracted to each other (positive collocates)

---

Q2. Why is Mutual Information (MI) biased toward rare words, and what is the practical implication?

MI bias is a bug in the implementation that can be fixed by updating the package
MI inflates for rare words because a single co-occurrence makes O11 >> E11 when E11 is very small; in practice, set minimum frequency thresholds (e.g., both words must occur ≥10 times) before using MI
MI only works with stopwords removed
MI is biased toward frequent words because it is based on raw counts

---

Keyword Analysis

Section Overview

What you’ll learn: How to identify words that are statistically over-represented in one corpus compared to a reference corpus — the method of keyword analysis

Key function: quanteda.textstats::textstat_keyness()

When to use it: When you want to characterise what makes one text or corpus distinctive; for comparing genres, authors, time periods, or groups

Keyword analysis identifies words that are key — statistically over-represented in a target corpus compared to a reference corpus. A keyword is not just a frequent word; it is a word whose frequency in the target corpus cannot be explained by its base rate in language generally (as captured by the reference corpus). Keywords characterise what is unusual, distinctive, or topically specific about a text.

The Keyword Analysis Logic

Like collocation analysis, keyword analysis uses a contingency table:

	Target corpus	Reference corpus
Word present	O₁₁	O₁₂
Other words	O₂₁	O₂₂

The null hypothesis is that the word’s relative frequency is the same in both corpora. Statistical tests (log-likelihood, chi-square, Fisher’s exact test) evaluate whether the observed difference is larger than expected by chance.

Setting Up: Target and Reference Corpora

# We will compare two chapters of Alice as a simple example  
# Target: Chapter 8 (the Queen's croquet ground — "Off with their heads!")  
# Reference: all other chapters combined  
  
# Identify which chapters are which  
cat("Chapter 8 begins:", substr(alice_chapters[9], 1, 80), "\n")  

Chapter 8 begins: chapter viii. the queen’s croquet-ground a large rose-tree stood near the entran 

# Target: chapter 9 (index 9 = chapter 8 after the preamble)  
target_text <- alice_chapters[9]  
  
# Reference: all other chapters  
reference_text <- paste(alice_chapters[-9], collapse = " ")  
  
# Create a two-document corpus with a grouping variable  
kw_corpus <- quanteda::corpus(  
  c(target_text, reference_text)  
)  
docvars(kw_corpus, "group") <- c("target", "reference")  

Computing Keyness

# Build DFM grouped by target vs reference  
kw_dfm <- kw_corpus |>  
  quanteda::tokens(remove_punct = TRUE) |>  
  quanteda::dfm() |>  
  quanteda::dfm_group(groups = group)  
  
# Compute keyness using log-likelihood (G2) — recommended for keyword analysis  
kw_results <- quanteda.textstats::textstat_keyness(  
  x         = kw_dfm,  
  target    = "target",  
  measure   = "lr"       # log-likelihood ratio (G2)  
)  

feature	G2	p	n_target	n_reference
queen	60.3609	0.0000	31	37
soldiers	32.0409	0.0000	9	1
gardeners	31.8253	0.0000	8	0
hedgehog	27.2322	0.0000	7	0
five	23.3106	0.0000	7	1
executioner	18.1234	0.0000	5	0
procession	18.1234	0.0000	5	0
three	17.6428	0.0000	11	15
game	15.2687	0.0001	7	5
seven	14.8236	0.0001	5	1
rose-tree	13.6309	0.0002	4	0
queen’s	12.6444	0.0004	5	2
flamingo	10.7339	0.0011	4	1
shouted	9.6871	0.0019	5	4
beheaded	9.2133	0.0024	3	0

Visualising Keywords

quanteda.textplots provides a dedicated plot for keyword results:

quanteda.textplots::textplot_keyness(  
  x           = kw_results,  
  n           = 20,                         # show top 20 on each side  
  color       = c("steelblue", "tomato"),   # positive / negative keywords  
  show_reference = TRUE  
)  

The plot shows:

• Blue bars (left): Keywords — words over-represented in the target chapter
  
• Red bars (right): Negative keywords — words over-represented in the reference (the rest of the novel)
  
• Bar length: Keyness score (log-likelihood G²)

Computing tf-idf

An alternative to log-likelihood keyness is tf-idf weighting, which assigns each word a score reflecting how characteristic it is of each document within the collection:

# Build raw count DFM with one document per chapter
chapter_dfm <- quanteda::corpus(alice_chapters) |>
  quanteda::tokens(remove_punct = TRUE) |>
  quanteda::dfm()

# Apply tf-idf weighting to the raw count DFM
tfidf_dfm <- quanteda::dfm_tfidf(chapter_dfm,
                                  scheme_tf = "count",
                                  scheme_df = "inverse")

# Extract top 15 most characteristic words for chapter 9
# Convert to a tidy data frame manually — one row per feature per document
chapter9_tfidf <- tfidf_dfm[9, ] |>                     # select chapter 9 row
  quanteda::convert(to = "data.frame") |>                # wide → data frame
  tidyr::pivot_longer(-doc_id,
                      names_to  = "feature",
                      values_to = "tfidf") |>            # wide → long
  dplyr::filter(tfidf > 0) |>
  dplyr::arrange(dplyr::desc(tfidf)) |>
  head(15)

feature	tf-idf score	chapter
queen	10.409555	text9
gardeners	8.911547	text9
hedgehog	7.797603	text9
soldiers	7.316220	text9
the	5.840034	text9
five	5.690393	text9
king	5.630717	text9
procession	5.569717	text9
executioner	5.569717	text9
rose-tree	4.455773	text9
seven	4.064567	text9
cat	3.734760	text9

Log-Likelihood vs. tf-idf: When to Use Each

Log-likelihood (G²) compares a target to a specific reference corpus using a significance test. It answers: Is this word’s frequency in my target significantly higher than in my reference? It is the standard method in corpus linguistics keyword analysis and is recommended when you have a clear comparison corpus.

tf-idf computes a weighting for each word in each document relative to the whole collection. It is most useful for information retrieval (finding the most characteristic terms in each document in a collection) and feature extraction for text classification. It does not require a separate reference corpus but is not directly interpretable as a significance test.

For most text analysis research questions (what makes text A distinctive compared to text B?), log-likelihood is the more appropriate method.

---

Exercises: Keyword Analysis

Q1. Why is the choice of reference corpus critical for keyword analysis?

The choice of reference corpus only matters when using tf-idf, not log-likelihood
The reference corpus must be larger than the target corpus for statistical reasons
The reference corpus determines the stopword list used
Keywords are defined relative to the reference corpus — a word is only a keyword if it is over-represented in the target compared to that specific reference. Different reference corpora produce different keywords for the same target text.

---

Q2. A keyword analysis returns the, and, and of as the top keywords (with very high G² scores). What likely caused this, and how should it be addressed?

The target and reference corpora are very different in size or genre, causing function word frequencies to differ systematically. Solutions include normalising by text length, using frequency-matched corpora, or filtering keywords by minimum frequency in both corpora.
The result is correct — function words are the most important keywords
Stopwords should be removed before keyword analysis
The log-likelihood measure is incorrect and should be replaced with tf-idf

---

Quick Reference

Section Overview

A compact reference for the most commonly used functions and workflows in this tutorial

Core Function Summary

Function	Package	Purpose
corpus(x)	quanteda	Create a corpus object from text
tokens(x, ...)	quanteda	Tokenise text into words
dfm(tokens)	quanteda	Create Document-Feature Matrix
kwic(tokens, pattern, window)	quanteda	Extract KWIC concordance
textstat_frequency(dfm)	quanteda.textstats	Compute word frequencies
textstat_keyness(dfm, target)	quanteda.textstats	Compute keyness statistics
dfm_tfidf(dfm)	quanteda	Compute tf-idf weights
fcm(tokens)	quanteda	Create Feature Co-occurrence Matrix
textplot_wordcloud(dfm)	quanteda.textplots	Plot word cloud
textplot_keyness(keyness)	quanteda.textplots	Plot keyword comparison
stopwords('english')	quanteda	Get built-in stopword list

Standard Text Analysis Workflow

# 1. Load and pre-process text  
text <- readLines("your_text.txt") |>  
  paste(collapse = " ") |>  
  tolower()  
  
# 2. Build quanteda objects  
corp   <- quanteda::corpus(text)  
toks   <- quanteda::tokens(corp, remove_punct = TRUE)  
toks_s <- quanteda::tokens_remove(toks, quanteda::stopwords("english"))  
dfm    <- quanteda::dfm(toks_s)  
  
# 3. Concordancing  
kwic_result <- quanteda::kwic(toks, pattern = "word", window = 5)  
  
# 4. Word frequency  
freq_table  <- quanteda.textstats::textstat_frequency(dfm)  
  
# 5. Collocations (sentence-level co-occurrence)  
fcm_result  <- quanteda::dfm(toks) |>  
                 quanteda::fcm(tri = FALSE)  
  
# 6. Keywords (requires target + reference corpus)  
kw_result   <- quanteda.textstats::textstat_keyness(  
                 dfm_grouped, target = "target")  

Citation & Session Info

Citation

Martin Schweinberger. 2026. Introduction to Text Analysis: Practical Implementation in R. The Language Technology and Data Analysis Laboratory (LADAL), The University of Queensland, Australia. url: https://ladal.edu.au/tutorials/textanalysis/textanalysis.html (Version 2026.03.28), doi: .

@manual{martinschweinberger2026introduction,
  author       = {Martin Schweinberger},
  title        = {Introduction to Text Analysis: Practical Implementation in R},
  year         = {2026},
  note         = {https://ladal.edu.au/tutorials/textanalysis/textanalysis.html},
  organization = {The Language Technology and Data Analysis Laboratory (LADAL), The University of Queensland, Australia},
  edition      = {2026.03.28}
  doi      = {}
}

sessionInfo()  

R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Australia/Brisbane
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] tidyr_1.3.2               ggraph_2.2.1             
 [3] quanteda.textplots_0.95   quanteda.textstats_0.97.2
 [5] wordcloud2_0.2.1          tidytext_0.4.2           
 [7] udpipe_0.8.11             tm_0.7-16                
 [9] NLP_0.3-2                 quanteda_4.2.0           
[11] flextable_0.9.7           ggplot2_3.5.1            
[13] stringr_1.5.1             dplyr_1.2.0              

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1        viridisLite_0.4.2       farver_2.1.2           
 [4] viridis_0.6.5           fastmap_1.2.0           tweenr_2.0.3           
 [7] fontquiver_0.2.1        janeaustenr_1.0.0       digest_0.6.39          
[10] lifecycle_1.0.5         tokenizers_0.3.0        magrittr_2.0.3         
[13] compiler_4.4.2          rlang_1.1.7             tools_4.4.2            
[16] igraph_2.1.4            yaml_2.3.10             sna_2.8                
[19] data.table_1.17.0       knitr_1.51              labeling_0.4.3         
[22] askpass_1.2.1           stopwords_2.3           graphlayouts_1.2.2     
[25] htmlwidgets_1.6.4       xml2_1.3.6              withr_3.0.2            
[28] purrr_1.0.4             grid_4.4.2              polyclip_1.10-7        
[31] gdtools_0.4.1           colorspace_2.1-1        scales_1.3.0           
[34] MASS_7.3-61             cli_3.6.4               rmarkdown_2.30         
[37] ragg_1.3.3              generics_0.1.3          rstudioapi_0.17.1      
[40] cachem_1.1.0            ggforce_0.4.2           network_1.19.0         
[43] splines_4.4.2           parallel_4.4.2          vctrs_0.7.1            
[46] Matrix_1.7-2            jsonlite_1.9.0          slam_0.1-55            
[49] fontBitstreamVera_0.1.1 ggrepel_0.9.6           systemfonts_1.2.1      
[52] glue_1.8.0              statnet.common_4.11.0   codetools_0.2-20       
[55] stringi_1.8.4           gtable_0.3.6            munsell_0.5.1          
[58] tibble_3.2.1            pillar_1.10.1           htmltools_0.5.9        
[61] openssl_2.3.2           R6_2.6.1                textshaping_1.0.0      
[64] tidygraph_1.3.1         evaluate_1.0.3          lattice_0.22-6         
[67] SnowballC_0.7.1         memoise_2.0.1           renv_1.1.1             
[70] fontLiberation_0.1.0    Rcpp_1.0.14             zip_2.3.2              
[73] uuid_1.2-1              fastmatch_1.1-6         coda_0.19-4.1          
[76] nlme_3.1-166            nsyllable_1.0.1         gridExtra_2.3          
[79] mgcv_1.9-1              officer_0.6.7           xfun_0.56              
[82] pkgconfig_2.0.3        

AI Transparency Statement

This tutorial was re-developed with the assistance of Claude (claude.ai), a large language model created by Anthropic. Claude was used to help revise the tutorial text, structure the instructional content, generate the R code examples, and write the checkdown quiz questions and feedback strings. All content was reviewed, edited, and approved by the author (Martin Schweinberger), who takes full responsibility for the accuracy and pedagogical appropriateness of the material. The use of AI assistance is disclosed here in the interest of transparency and in accordance with emerging best practices for AI-assisted academic content creation.

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