text_analysis

library(tidyverse)

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✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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library(tidytext)        
library(readxl)

inaug_speeches <- read_excel("inaug_speeches.xlsx")

inaug_words <- inaug_speeches |>
  unnest_tokens(word, text)

Below you will find the coding and results of several measures of complexity, including number of words, number of distinct words, the density, and a graph of the distributions of word lengths for each author

inaug_words |> 
  group_by(author) |> 
  summarize(num_words = n(), lex_diversity = n_distinct(word), lex_density = n_distinct(word))

# A tibble: 7 × 4
  author     num_words lex_diversity lex_density
  <chr>          <int>         <int>       <int>
1 FDR             1881           709         709
2 Jefferson       1730           680         680
3 Kennedy         1365           534         534
4 Lincoln         3637          1011        1011
5 Obama           2399           893         893
6 Reagan          2442           845         845
7 Washington      1420           593         593

inaug_words |>
  mutate(word_length = nchar(word)) |> 
  distinct(word, word_length, author) |> 
  arrange(-word_length)

# A tibble: 5,265 × 3
   word             word_length author    
   <chr>                  <int> <chr>     
 1 unconstitutional          16 Lincoln   
 2 constitutionally          16 Lincoln   
 3 misunderstanding          16 Reagan    
 4 administration's          16 Reagan    
 5 irresponsibility          16 Obama     
 6 recommendations           15 Washington
 7 representatives           15 Washington
 8 administrations           15 Jefferson 
 9 insurrectionary           15 Lincoln   
10 misconstruction           15 Lincoln   
# ℹ 5,255 more rows

inaug_words |>
  group_by(author) |> 
  mutate(word_length = nchar(word)) |> 
  summarize(mean_word_length = mean(word_length)) |> 
  arrange(-mean_word_length)

# A tibble: 7 × 2
  author     mean_word_length
  <chr>                 <dbl>
1 Washington             4.94
2 Jefferson              4.74
3 FDR                    4.68
4 Lincoln                4.66
5 Reagan                 4.47
6 Obama                  4.44
7 Kennedy                4.39

inaug_words |>
  mutate(word_length = nchar(word)) |> 
  ggplot(aes(word_length)) +
  facet_wrap(vars(author), scales = "free_y")

  binwidth = 1 
  labs(title = "word length")

$title
[1] "word length"

attr(,"class")
[1] "labels"

inaug_words |> 
  group_by(author) |>
  count(word, sort = T)

# A tibble: 5,265 × 3
# Groups:   author [7]
   author     word      n
   <chr>      <chr> <int>
 1 Lincoln    the     256
 2 Lincoln    of      146
 3 Lincoln    to      135
 4 FDR        the     130
 5 Jefferson  the     130
 6 Obama      the     130
 7 Reagan     the     123
 8 Obama      and     114
 9 Washington the     114
10 FDR        of      109
# ℹ 5,255 more rows

stop_words |> 
  filter(lexicon == "snowball") -> snowball

Below you will find two graphs with both the most common words used by each author, and the the most common words with stop words removed.

inaug_words |>
  group_by(author) %>% 
  count(word, sort = T) %>% 
  top_n(5) |> 
  ungroup() |> 
  mutate(word = reorder(word, n)) |>
  ggplot(aes(word, n, fill = author)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "Most common words") +
  facet_wrap(vars(author), scales = "free") +
  scale_fill_viridis_d() +
  theme_minimal() +
  coord_flip()

Selecting by n

graph with stop words removed

inaug_words |>
  anti_join(snowball) %>% 
  group_by(author) %>% 
  count(word, sort = T) %>% 
  top_n(5) |> 
  ungroup() |> 
  mutate(word = reorder(word, n)) |>
  ggplot(aes(word, n, fill = author)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "Most common words") +
  facet_wrap(vars(author), scales = "free") +
  scale_fill_viridis_d() +
  theme_minimal() +
  coord_flip()

Joining with `by = join_by(word)`
Selecting by n

Below you will find the code and table of TF-IDF

inaug_word_counts <- inaug_speeches |>            
  unnest_tokens(word, text) |>
  count(author, word, sort = TRUE) 

total_words <- inaug_word_counts |>               
  group_by(author) |> 
  summarize(total = sum(n))

inaug_word_counts <- left_join(inaug_word_counts, total_words)

Joining with `by = join_by(author)`

inaug_tf_idf <- inaug_word_counts |>             
  bind_tf_idf(word, author, n)

inaug_tf_idf |>                                   
  arrange(-tf_idf)

# A tibble: 5,265 × 7
   author    word           n total      tf   idf  tf_idf
   <chr>     <chr>      <int> <int>   <dbl> <dbl>   <dbl>
 1 FDR       helped         7  1881 0.00372 1.95  0.00724
 2 FDR       leadership     7  1881 0.00372 1.95  0.00724
 3 Lincoln   while         13  3637 0.00357 1.95  0.00696
 4 Kennedy   both          10  1365 0.00733 0.847 0.00621
 5 Kennedy   arms           4  1365 0.00293 1.95  0.00570
 6 FDR       money          5  1881 0.00266 1.95  0.00517
 7 Kennedy   sides          8  1365 0.00586 0.847 0.00497
 8 Lincoln   case           9  3637 0.00247 1.95  0.00482
 9 Lincoln   union         20  3637 0.00550 0.847 0.00466
10 Jefferson principle      6  1730 0.00347 1.25  0.00434
# ℹ 5,255 more rows

Graph of TF-IDF.

inaug_tf_idf  |>
  arrange(-tf_idf) |>
  mutate(word = factor(word, levels = rev(unique(word)))) |> 
  group_by(author) |> 
  top_n(5) |> 
  ggplot(aes(word, tf_idf, fill = author)) +
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = "tf-idf") +
  facet_wrap(~author, scales = "free") +
  coord_flip()

Selecting by tf_idf

theme_minimal()

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scale_fill_viridis_d()

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labs(title = "Most distinctive words in each inaugaration note")

$title
[1] "Most distinctive words in each inaugaration note"

attr(,"class")
[1] "labels"

A Sentiment Analysis was completed as seen below. A graph and table can be seen of the most common positive and negative words

Table:

bing <- get_sentiments("bing")
bing

# A tibble: 6,786 × 2
   word        sentiment
   <chr>       <chr>    
 1 2-faces     negative 
 2 abnormal    negative 
 3 abolish     negative 
 4 abominable  negative 
 5 abominably  negative 
 6 abominate   negative 
 7 abomination negative 
 8 abort       negative 
 9 aborted     negative 
10 aborts      negative 
# ℹ 6,776 more rows

inaug_words |> 
  inner_join(bing) |> 
  count(word, sentiment, sort = TRUE)

Joining with `by = join_by(word)`

# A tibble: 619 × 3
   word       sentiment     n
   <chr>      <chr>     <int>
 1 good       positive     21
 2 freedom    positive     19
 3 great      positive     19
 4 right      positive     18
 5 work       positive     18
 6 peace      positive     17
 7 free       positive     16
 8 well       positive     15
 9 confidence positive     11
10 happiness  positive     11
# ℹ 609 more rows

Graph:

inaug_words |> 
  inner_join(bing) |> 
  count(word, sentiment, sort = TRUE) %>% 
  group_by(sentiment) |>
  top_n(10) |>
  ungroup() |>
  mutate(word = reorder(word, n)) |>
  ggplot(aes(word, n, fill = sentiment)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(vars(sentiment), scales = "free") +
  labs(y = "Inaugaration notes: Words that contribute the most to each sentiment",
       x = NULL) +
  scale_fill_viridis_d() +
  coord_flip() +
  theme_minimal()

Joining with `by = join_by(word)`
Selecting by n

Below you will find tables of the most common bigrams, and the most common bigrams after removing stop words.

inaug_speeches |>
  unnest_tokens(bigram, text, token = "ngrams", n = 2) |> 
  select(bigram) -> inaug_bigrams

inaug_bigrams %>% 
  count (bigram, sort = T)

# A tibble: 10,876 × 2
   bigram       n
   <chr>    <int>
 1 of the     146
 2 in the      80
 3 of our      55
 4 to the      55
 5 and the     38
 6 to be       37
 7 it is       35
 8 by the      34
 9 for the     30
10 that the    29
# ℹ 10,866 more rows

Stop Words Removed

inaug_bigrams %>% 
  count (bigram, sort = T) %>% 
    separate(bigram, c("word1", "word2"), sep = " ") |> 
  filter(!word1 %in% snowball$word) |>
  filter(!word2 %in% snowball$word) |> 
  unite(bigram, word1, word2, sep = " ")

# A tibble: 2,399 × 2
   bigram                 n
   <chr>              <int>
 1 let us                18
 2 fellow citizens       16
 3 united states         11
 4 american people        6
 5 federal government     4
 6 government can         4
 7 one section            4
 8 vice president         4
 9 will endure            4
10 among us               3
# ℹ 2,389 more rows

Below you will find a graph showing the most common words that came after the words “i”, “live”, and “lead”.

first_word <- c("i", "live", "lead")                                 

inaug_bigrams |> 
  count(bigram, sort = T) |> 
  separate(bigram, c("word1", "word2"), sep = " ") |>       
  filter(word1 %in% first_word) |>                         
  count(word1, word2, wt = n, sort = TRUE) %>% 
  mutate(word2 = factor(word2, levels = rev(unique(word2)))) |>   
  group_by(word1) |> 
  top_n(5) |> 
  ggplot(aes(word2, n, fill = word1)) +                          
  scale_fill_viridis_d() +                                         
  geom_col(show.legend = FALSE) +
  labs(x = NULL, y = NULL, title = "Word following:") +
  facet_wrap(~word1, scales = "free") +
  coord_flip() +
  theme_minimal()

Selecting by n