week9.qmd

---
title: "ETC1010/ETC5510: Introduction to Data Analysis"
title-slide-attributes: 
  data-background-image: "_extensions/monash/images/bg-03.png"
subtitle: "Week 9: Text Analysis"
author: 
 - name: "Patrick Li"
   email: "patrick.li@monash.edu"
institute: "Department of Econometrics and Business Statistics"
footer: "ETC1010/ETC5510 Lecture 9 | Melbourne time <span id = 'mel-local-time'></span>"
format: 
  monash-revealjs:
    multiplex: false
    slide-number: c/t
    slide-tone: false
    width: 1600
    height: 900
    margin: 0.05
    transition: fade
    transition-speed: fast
    embed-resources: true
webr:
  show-startup-message: false
  packages: ['tidyverse', 'tidytext', 'textdata', 'gutenbergr', 'stopwords']
  autoload-packages: true
  cell-options:
    editor-font-scale: 0.6
    editor-max-height: 120
    autorun: true
filters: 
  - webr
editor_options: 
  chunk_output_type: console
---

```{r, include = FALSE}
current_file <- knitr::current_input()
basename <- gsub(".[Rq]md$", "", current_file)

knitr::opts_chunk$set(
  fig.path = sprintf("images/%s/", basename),
  fig.width = 6,
  fig.height = 4,
  fig.align = "center",
  out.width = "100%",
  fig.retina = 3,
  echo = TRUE,
  warning = FALSE,
  message = FALSE,
  cache = TRUE,
  cache.path = "cache/"
)

library(tidyverse)
library(tidytext)
library(textdata)
library(gutenbergr)
```

---

## `r fontawesome::fa("lightbulb")` Recap

- What is web scraping?
- `rvest` and `polite`
- What is a function?
- File paths and RStudio projects


---

## `r fontawesome::fa("sitemap")` Outline

1. Regular expression
2. Why do we want to analyze text data?
3. Steps for text analysis
4. R packages for text analysis
5. Tidy text
6. Stop words
7. Sentiment of the text
8. Word Importance

---

## Regular Expression {.transition-slide .center style="text-align: center;"}

---

## `r fontawesome::fa("search")` Regular Expression

**Regular expressions** provide a concise and flexible way to define patterns in strings. 

At their most basic level, they can be used to match a **fixed string**, allowing the pattern to appear anywhere **from one to multiple times** within a single string.

::: {.columns}

::: {.column}


```{webr-r}
str_view(fruit, "berry")
str_view(fruit, "r")
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

## `str_view()`

We will use `stringr::str_view()` to demonstrate various regular expression syntax.

The `str_view()` function highlights matching patterns by enclosing them in `<>`.


:::

:::

:::


---

## `r fontawesome::fa("search")` Metacharacter


**Metacharacters** have special meaning in regular expression.

::: {.columns}

::: {.column}




```{webr-r}
#| editor-max-height: 200
str_view(fruit, "r.")
str_view(fruit, "^b")
str_view(fruit, "y$")
str_view(fruit, "^a|^b")
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

## Common Metacharacters (1/3)

- `.`: match **any character** except for `\n`
- `^`: match the **starting position** within the string
- `$`: match the **ending position** of the string
- `|`: match the expression before or the expression after the operator


:::

:::

:::

---


## `r fontawesome::fa("search")` Metacharacter

**Quantifiers** control how many times a pattern matches.

::: {.columns}

::: {.column}


```{webr-r}
#| editor-max-height: 200
str_view(fruit, "^ap*")
str_view(fruit, "e{1,2}$")
str_view(fruit, "^ap?")
str_view(fruit, "ap.+")
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

## Common Metacharacters (2/3)


- `*`: match the preceding element **zero or more times**
- `{m,n}`: match the preceding element **at least $m$ and not more than $n$ times**
- `?`: match the preceding element **zero or one time**
- `+`: match the preceding element **one or more times**


:::

:::

:::


---

## `r fontawesome::fa("search")` Metacharacter

A **character set**, allows you to match any character in a set. 

**Remember, `\` needs to be escaped.**

::: {.columns}

::: {.column}


```{webr-r}
#| editor-max-height: 200
str_view(fruit, "[abc]")
str_view(fruit, "[^a-z]")
str_view(fruit, "\\w+")
str_view(fruit, "a[cp]")
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

## Common Metacharacters (3/3)


- `[]`: match **a single character** that is **contained** within the brackets
- `[^]`: match **a single character** that is **not contained** within the brackets
- `[a-z]`: match any **lower case letter**
- `[A-Z]`: match any **upper case letter**
- `[0-9]`: match any **number**
- `\d`: match any **digit**
- `\w`: match any **word** character (**letter** and **number**)


:::

:::

:::


---

## `r fontawesome::fa("search")` Grouping and Capturing

Parentheses create **capturing groups**, enabling you to work with specific subcomponents of a match.

::: {.columns}

::: {.column}


```{webr-r}
#| editor-max-height: 200
str_view(fruit, "([a-z])\\1")
str_replace(c("AUD 1", "USD 15", "NZD 6.4"),
            "(^[A-Z]*) *(.*)",
            "$\\2 (\\1)")
```

:::

::: {.column}

::: {.callout-tip .no-top-margin-callout}


You can reuse these groups in your pattern, where `\1` refers to the match inside the first set of parentheses, `\2` refers to the second, and so forth.

- `.*` means 0 or more of any character


:::

:::

:::

---


## Text Analysis {.transition-slide .center style="text-align: center;"}

---

## `r fontawesome::fa("envelope-open-text")` What is Text Analysis?

Text analysis is a set of techniques that enable data analysts to **extract and quantify information stored in the text**, whether it's from *messages*, *tweets*, *emails*, *books*, or other sources.

::: {.callout-note}

## For example:

- Predicting Melbourne house prices based on realtor descriptions.
- Gauging public discontent with Melbourne train stoppages using Twitter data.
- Identifying differences between the first and sixth editions of Darwin's *Origin of the Species*.
- Analyzing and quantifying sentiment within a given text.

:::

---

## `r fontawesome::fa("hourglass-start")` Text Analysis Process

We will use the `tidytext` package for the first three steps and the `gutenbergr` package to obtain text data.

1. Import the text.
2. Pre-process the data by removing less meaningful words, known as **stop words**.
3. Tokenize the text by breaking it into **words**, **sentences**, **n-grams**, or chapters.
4. Summarize the results.
5. Apply modeling techniques.

---

## <img src="images/tidytext.png" class="png-icon"> `tidytext`

Using [tidy data principles](https://www.tidytextmining.com/) can make many text mining tasks **easier**,
**more effective**, and consistent with tools already in wide use.

Lets' start with a conversation from *Game of Thrones*:

```{webr-r}
#| editor-max-height: 400
text <- c("What is it that you want, exactly?",
          "Peace. Prosperity",
          "A land where the powerful do not prey on the powerless",
          "Where the castles are made of gingerbread",
          "and the moats are filled with blackberry wine",
          "The powerful have always preyed on the powerless",
          "that’s how they became powerful in the first place",
          "Perhaps ",
          "And perhaps we’ve grown so used to horror",
          "we assume there’s no other way")
```


---

## <img src="images/tidytext.png" class="png-icon"> What is Tidy Text Format?

**Tidy text format is a table with one-token-per-row.**

A token is a **meaningful unit of text**, such as a **word**, that we are interested in using for analysis.

**Tokenization** (`unnest_tokens()`) is the process of **splitting text into tokens**. 

```{webr-r}
tibble(line = seq_along(text), text = text) %>%
  unnest_tokens(output = word, input = text, token = "words")
```

## <img src="images/tidytext.png" class="png-icon"> Unit for Tokenization - Characters

Use **characters** as tokens.

```{webr-r}
tibble(line = seq_along(text), text = text) %>%
  unnest_tokens(output = word, input = text, token = "characters")
```

## <img src="images/tidytext.png" class="png-icon"> Unit for Tokenization - Ngrams

**Ngrams** are groups of words define by `n`. 

```{webr-r}
tibble(line = seq_along(text), text = text) %>%
  unnest_tokens(output = word, input = text, token = "ngrams", n = 2)
```

## `r fontawesome::fa("otter")` Analyzing User Reviews for *Animal Crossing: New Horizons* (A Nintendo Game)

The dataset consists of user and critic reviews for [*Animal Crossing: New Horizons*](https://www.nintendo.com/games/detail/animal-crossing-new-horizons-switch/), scraped from [Metacritic](https://www.metacritic.com/game/animal-crossing-new-horizons/).

This data was sourced from a [#TidyTuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-05-05/readme.md) challenge.

```{webr-r}
#| message: false
acnh_user_reviews <- read_tsv("https://raw.githubusercontent.com/numbats/ida2024s2/master/data/acnh_user_reviews.tsv")
glimpse(acnh_user_reviews)
```

## `r fontawesome::fa("wave-square")` Grade Distribution

::: {.columns}

::: {.column}

```{webr-r}
#| message: false
ggplot(acnh_user_reviews) +
  geom_histogram(aes(grade))
```

:::

::: {.column}

::: {.callout-warning .no-top-margin-callout}

A value of 0 could indicate missing data!

:::

:::

:::

## `r fontawesome::fa("circle-plus")` Positive Reviews

```{webr-r}
#| editor-max-height: 300
set.seed(1999)
acnh_user_reviews %>%
  filter(grade > 8) %>%
  sample_n(3) %>%
  .$text
```

## `r fontawesome::fa("circle-minus")` Negative Reviews

```{webr-r}
#| editor-max-height: 300
set.seed(2099)
acnh_user_reviews %>%
  filter(grade == 0) %>%
  sample_n(3) %>%
  .$text
```

## `r fontawesome::fa("trash")` Remove the "Expand" from the Text

Long reviews are compressed from the scraping procedure. 

We will remove these characters from the text.

```{webr-r}
acnh_user_reviews_parsed <- acnh_user_reviews %>%
  mutate(text = str_remove(text, "Expand$"))
```

## `r fontawesome::fa("broom")` Tidy up the Reviews

Use `unnest_tokens()` to convert the data into **tidy text format**. 

```{webr-r}
user_reviews_words <- acnh_user_reviews_parsed %>%
  unnest_tokens(output = word, input = text)
user_reviews_words
```

## `r fontawesome::fa("wave-square")` Distribution of Words Per Review

::: {.columns}

::: {.column}

```{webr-r}
#| message: false
#| editor-max-height: 300
user_reviews_words %>%
  count(user_name) %>%
  ggplot(aes(x = n)) +
  geom_histogram() +
  labs(x = "Number of words used per review",
       y = "Number of users")
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

- 58% of reviewers write fewer than 75 words, while 36% write more than 150 words.

- most users tend to provide brief feedback, while a smaller group of more engaged reviewers write longer, more detailed responses.

:::

:::

:::

## `r fontawesome::fa("fire")` Most Common Words



::: {.columns}

::: {.column}

```{webr-r}
user_reviews_words %>%
  count(word, sort = TRUE)
```

:::

::: {.column}

::: {.callout-note .no-top-margin-callout}

Certain common words, such as "the" and "a," don't contribute much meaning to the text.

:::

:::

:::

---

## `r fontawesome::fa("ban")` Stop Words

In computing, **stop words** are words that are filtered out before or after processing natural language data (text).

These words are generally among the **most common in a language**, but there is no universal list of stop words used by all natural language processing tools.

While stop words often **do not add meaning to the text**, they do contribute to its grammatical structure.

---

## `r fontawesome::fa("ban")` English Stop Words

**Lexicon**: a word book or reference word book.

```{webr-r}
get_stopwords()
```

---

## `r fontawesome::fa("ban")` Chinese Stop Words

```{webr-r}
get_stopwords(language = "zh", source = "misc")
```

---

## `r fontawesome::fa("book")` Various Lexicons

See `?get_stopwords` for more info.

```{webr-r}
stopwords_getsources()
get_stopwords(source = "smart")
```

---

## `r fontawesome::fa("book")` Comparing Lexicons by the Number of Stopwords

It is perfectly acceptable to start with a pre-made word list and remove or append additional words according to your particular use case. 

```{webr-r}
nrow(get_stopwords(source = "smart"))
nrow(get_stopwords(source = "snowball"))
nrow(get_stopwords(source = "stopwords-iso"))
```

---

## `r fontawesome::fa("trash")` Remove Stopwords

You can replace `filter()` with an `anti_join()` call, but `filter()` makes the action clearer.

::: {.columns}

::: {.column width=60%}

```{webr-r}
#| editor-max-height: 300
stopwords_smart <- get_stopwords(source = "smart")
user_reviews_words %>%
  filter(!word %in% stopwords_smart$word) %>%
  count(word, sort = TRUE)
```

:::

::: {.column width=40%}


::: {.callout-note .no-top-margin-callout}

The most common words are fitting, as the **game** is a popular **Nintendo** title for the **Switch console**, where **players** can create and **play** on their own **island** paradise with **animal** villagers.

:::

:::

:::

---

## `r fontawesome::fa("chart-bar")` Frequency of Words in User Reviews



::: {.columns}

::: {.column}

```r
user_reviews_words %>%
  anti_join(stopwords_smart) %>%
  count(word) %>%
  arrange(-n) %>%
  top_n(20) %>%
  ggplot(aes(fct_reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_minimal() +
  labs(title = "Frequency of words in user reviews",
  subtitle = "",
  y = "",
  x = "")
```

:::


::: {.column}

```{r echo = FALSE}
acnh_user_reviews <- read_tsv("https://raw.githubusercontent.com/numbats/ida2024s2/master/data/acnh_user_reviews.tsv")
acnh_user_reviews_parsed <- acnh_user_reviews %>%
  mutate(text = str_remove(text, "Expand$"))
user_reviews_words <- acnh_user_reviews_parsed %>%
  unnest_tokens(output = word, input = text)
stopwords_smart <- get_stopwords(source = "smart")
user_reviews_words %>%
  anti_join(stopwords_smart) %>%
  count(word) %>%
  arrange(-n) %>%
  top_n(20) %>%
  ggplot(aes(fct_reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_minimal() +
  labs(title = "Frequency of words in user reviews",
  subtitle = "",
  y = "",
  x = "")
```

:::

:::

---

## Let's have a break! {.transition-slide .center style="text-align: center;"}

---

## Sentiment Analysis {.transition-slide .center style="text-align: center;"}

---

## `r fontawesome::fa("face-tired")` Sentiment Lexicons

**Sentiment analysis** is the process of determining the **emotional tone or opinion expressed in a piece of text**. 

It is commonly used to analyze customer feedback, reviews, and social media.

::: {.callout-note}

## Three widely used **general-purpose lexicons** for **sentiment analysis** are:

1. **AFINN**: developed by Finn Årup Nielsen, which assigns words a **score ranging from -5 to 5**, with **negative scores reflecting negative sentiment** and **positive scores reflecting positive sentiment**.
2. **bing**: created by Bing Liu and collaborators, which classifies words into two simple categories: **positive or negative**.
3. **nrc**: by Saif Mohammad and Peter Turney, which categorizes words into emotions such as **anger**, **anticipation**, **disgust**, **fear**, **joy**, **sadness**, **surprise**, and **trust**, in addition to **positive** and **negative** sentiment.

All three lexicons are based on **unigrams** (**single word**).


:::

---

## `r fontawesome::fa("face-tired")` Sentiment Analysis

- One approach to analyzing text sentiment is to treat the text as a combination of individual words.
- The overall sentiment is determined by **summing the sentiment values of the individual words**.
- However, this method can be inaccurate as it overlooks **context**, **word order**, and the impact of **phrases that can change the meaning of individual words**.
- Solution: Machine learning applied to large-scale text datasets can address these challenges. You will explore these concepts in ETC3250/ETC5250 and ETC3555/ETC5555.

---

## `r fontawesome::fa("book")` Sentiment Lexicons

::: {style="display: none"}

```{webr-r}
#| context: setup
get_sentiments <- function(dict_name) {
  read_rds(glue::glue("https://raw.githubusercontent.com/numbats/ida2024s2/master/data/{dict_name}.rds"))
}
```

:::

Use `get_sentiments()` to get the Lexicons.

```{webr-r}
get_sentiments("afinn")
get_sentiments("bing")
get_sentiments("nrc")
```


---

## `r fontawesome::fa("face-tired")` Sentiments in the Reviews

`inner_join()` return rows from reviews if the word can be found in the Lexicon.

```{webr-r}
#| editor-max-height: 300
sentiments_bing <- get_sentiments("bing")
user_reviews_words %>%
  inner_join(sentiments_bing) %>%
  count(sentiment, word, sort = TRUE)
```

---

## `r fontawesome::fa("eye")` Visualizing Sentiments

```{r echo = FALSE}
sentiments_bing <- get_sentiments("bing")
```




::: {.columns}

::: {.column}

```r
user_reviews_words %>%
  inner_join(sentiments_bing) %>%
  count(sentiment, word, sort = TRUE) %>%
  arrange(desc(n)) %>%
  group_by(sentiment) %>%
  top_n(10) %>%
  ungroup() %>%
  ggplot(aes(fct_reorder(word, n), n, fill = sentiment)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~sentiment, scales = "free") +
  theme_minimal() +
  labs(title = "Sentiments in user reviews", x = "") 
```

:::


::: {.column}

```{r echo = FALSE}
user_reviews_words %>%
  inner_join(sentiments_bing) %>%
  count(sentiment, word, sort = TRUE) %>%
  arrange(desc(n)) %>%
  group_by(sentiment) %>%
  top_n(10) %>%
  ungroup() %>%
  ggplot(aes(fct_reorder(word, n), n, fill = sentiment)) +
  geom_col() +
  coord_flip() +
  facet_wrap(~sentiment, scales = "free") +
  theme_minimal() +
  labs(
  title = "Sentiments in user reviews",
  x = ""
  ) 
```

:::

:::

---

## `r fontawesome::fa("face-laugh-beam")` Average Sentiment Per Review

The average sentiment per review improves as the grade increases.

```{webr-r}
#| editor-max-height: 400
#| warning: false
#| editor-font-scale: 0.5
inner_join(user_reviews_words, sentiments_bing) %>%
  group_by(user_name) %>%
  summarise(grade = factor(first(grade), 0:10), ave_sentiment = mean(sentiment == "positive")) %>%
  ggplot() +
  geom_boxplot(aes(grade, ave_sentiment))
```


---

## `r fontawesome::fa("fire")` Common Words over Grades

Some common words appear in both very positive and very negative reviews, so how do we determine their importance?

```{webr-r}
user_reviews_words %>%
  filter(!word %in% stopwords_smart$word) %>%
  count(grade, word, sort = TRUE) 
```

---


## Word Importance {.transition-slide .center style="text-align: center;"}

---

## `r fontawesome::fa("star")` Word Importance

How do we measure the **importance of a word to a document in a collection of documents**?

For example a novel in a collection of novels or a review in a set of reviews...

We combine the following statistics:

- **Term frequency**
- **Inverse document frequency**

---

## `r fontawesome::fa("calculator")` Term Frequency

The **raw frequency** of a word $w$ in a document $d$. It is a function of
the word and the document.

<br>

$$
tf(w, d) = \frac{\text{count of } w \text{ in } d}{\text{total number of words in } d}
$$

<br>

The **term frequency for each word** is the number of times that word occurs divided by the total number of words in the document.

---

## `r fontawesome::fa("calculator")` Term Frequency

For our reviews **a document is a single user's review**. [More about that here.](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)

```{webr-r}
#| editor-max-height: 300
user_reviews_words %>%
  filter(!word %in% stopwords_smart$word) %>%
  filter(user_name == "Discoduckasaur") %>%
  count(word, sort = TRUE) %>%
  mutate(tf = n / sum(n)) %>%
  arrange(desc(tf)) 
```

---

## `r fontawesome::fa("calculator")` Inverse Document Frequency

The **inverse document frequency** tells how common or rare a word is
**across a collection of documents**. It is a function of a word $w$, and
the collection of documents $\mathcal{D}$.

<br>

$$
idf(w, \mathcal{D}) = \log\left(\frac{\text{size of } \mathcal{D}}{\text{number of documents that contain }w}\right)
$$

<br>

If every document contains $w$, then $\log(1) = 0$.




---

## `r fontawesome::fa("calculator")` Inverse Document Frequency

For the reviews data set, our collection is all the reviews. You could
compute this in a somewhat roundabout as follows:

```{webr-r}
#| editor-max-height: 300
#| warning: false
user_reviews_words %>%
  filter(!word %in% stopwords_smart$word) %>%
  mutate(collection_size = n_distinct(user_name)) %>%
  group_by(collection_size, word) %>%
  summarise(times_word_used = n_distinct(user_name)) %>%
  mutate(freq = collection_size / times_word_used, idf = log(freq)) 
```

---

## `r fontawesome::fa("calculator")` All together: Term Frequency, Inverse Document Frequency

Multiply `tf` and `idf` together. This is a function of a word $w$, a
document $d$, and the collection of documents $\mathcal{D}$:

<br>

$$
tf\_idf(w, d, \mathcal{D}) = tf(w, d) \times idf(w,\mathcal{D})
$$

<br>

A high `tf_idf` value indicates that a word **appears frequently in a specific document but is relatively rare across all documents**. 

Conversely, a low `tf_idf` value means the word **occurs in many documents**, causing the `idf` to approach zero and resulting in a small `tf_idf`.

---

## `r fontawesome::fa("calculator")` `tf_idf`

we can use `tidytext` to compute those values:

```{webr-r}
#| editor-max-height: 300
user_reviews_words %>%
  filter(!word %in% stopwords_smart$word) %>%
  count(user_name, word, sort = TRUE) %>%
  bind_tf_idf(term = word, document = user_name, n = n)
```


---

## `r fontawesome::fa("face-smile-wink")` What Words Were Important to (A Sample of) Users that Had Positive Reviews?


::: {.panel-tabset}

## `r fontawesome::fa("chart-bar")` Plot

```{r echo = FALSE, out.width="50%"}
user_reviews_words %>%
  anti_join(stopwords_smart) %>%
  count(user_name, word, sort = TRUE) %>%
  bind_tf_idf(term = word, document = user_name, n = n) %>%
  arrange(user_name, desc(tf_idf)) %>%
  filter(user_name %in% c("Alucard0", "Cbabybear", "TheRealHighKing")) %>%
  group_by(user_name) %>%
  top_n(5) %>%
  mutate(rank = paste("Top", 1:n())) %>%
  ungroup() %>%
  mutate(word = interaction(rank, word, lex.order = TRUE, sep = " : ")) %>%
  mutate(word = `levels<-`(rev(word), rev(levels(word)))) %>%
  ggplot() +
  geom_col(aes(word, tf_idf)) +
  facet_wrap(~user_name, ncol = 1, scales = "free_y") +
  coord_flip()
```

## `r fontawesome::fa("code")` Code

```r
user_reviews_words %>%
  anti_join(stopwords_smart) %>%
  count(user_name, word, sort = TRUE) %>%
  bind_tf_idf(term = word, document = user_name, n = n) %>%
  arrange(user_name, desc(tf_idf)) %>%
  filter(user_name %in% c("Alucard0", "Cbabybear", "TheRealHighKing")) %>%
  group_by(user_name) %>%
  top_n(5) %>%
  mutate(rank = paste("Top", 1:n())) %>%
  ungroup() %>%
  mutate(word = interaction(rank, word, lex.order = TRUE, sep = " : ")) %>%
  mutate(word = `levels<-`(rev(word), rev(levels(word)))) %>%
  ggplot() +
  geom_col(aes(word, tf_idf)) +
  facet_wrap(~user_name, ncol = 1, scales = "free_y") +
  coord_flip()
```

:::

---

## `r fontawesome::fa("book-open-reader")` Practice in Your Own Time

**Text Mining with R** has an example comparing historical physics textbooks:

> Discourse on Floating Bodies by Galileo Galilei, Treatise on Light by
> Christiaan Huygens, Experiments with Alternate Currents of High
> Potential and High Frequency by Nikola Tesla, and Relativity: The
> Special and General Theory by Albert Einstein. All are available on the
> Gutenberg project.

Work your way through the [comparison of physics books](https://www.tidytextmining.com/tfidf.html#a-corpus-of-physics-texts). It is
section 3.4.

---

## `r fontawesome::fa("link")` Resoruces: Thanks

- [Dr. Mine Çetinkaya-Rundel](https://rstudio-education.github.io/datascience-box/course-materials/slides/u5-d01-text-analysis/u5-d01-text-analysis.html#1)
- Dr. Julia Silge: [https://github.com/juliasilge/tidytext-tutorial](https://github.com/juliasilge/tidytext-tutorial) and
[https://juliasilge.com/blog/animal-crossing/](https://juliasilge.com/blog/animal-crossing/)
- Dr. Julia Silge and Dr. David Robinson:
[https://www.tidytextmining.com/](https://www.tidytextmining.com/)