# A tibble: 14,874 × 2
author word
<chr> <chr>
1 Washington among
2 Washington the
3 Washington vicissitudes
4 Washington incident
5 Washington to
6 Washington life
7 Washington no
8 Washington event
9 Washington could
10 Washington have
# ℹ 14,864 more rows
Above are bar-graphs showing the lexical length of each of the presidents inauguration speeches.
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) +coord_flip() +facet_wrap(~author, scales ="free") +scale_fill_viridis_d() +theme_minimal() +labs(x =NULL, y ="Most Frequently Used Words In Presidential Inaugural Speeches")
Selecting by n
The above graphs list the president and what the most common words they used in their inauguration speech.
stop_words %>%filter(lexicon =="snowball") -> snowballinaug_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 ="Commonality of Inauguration Speech Vocabulary") +facet_wrap(vars(author), scales ="free") +scale_fill_viridis_d() +theme_minimal() +coord_flip()
Joining with `by = join_by(word)`
Selecting by n
The above graphs list the president and what the most common words they used in their inauguration speech while dis-including common words such as: the, and, of, which, etc.
inaug_word_counts <- speech %>%unnest_tokens(word, text) %>%count(author, word, sort =TRUE)
# 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
The table highlights frequency of word used during a president’s speech (tf) compared to the number of times it was used in other speeches (idf) while also showing the significance the word holds to their particular speech when compared to its use in other inaugural speeches. .
inaug_tf_idk %>%arrange(-tf_idf) %>%mutate(word =factor(word, levels =rev(unique(word)))) %>%group_by(author) %>%top_n(3) %>%ggplot(aes(word, tf_idf, fill = author)) +geom_col(show.legend =FALSE) +labs(x =NULL, y ="tf-idf") +facet_wrap(~author, scales ="free") +theme_minimal() +scale_fill_viridis_d() +labs(title ="Lexicon in Inaugural Speeches Based on Uniqueness") +coord_flip()
Selecting by tf_idf
The graphs show the most common “unique” words that each of the listed president’s used in the inaugural speeches.
bing <-get_sentiments("bing") 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 ="Most Common Positive and Negative Words Used", x =NULL) +scale_fill_viridis_d() +coord_flip() +theme_minimal()
Joining with `by = join_by(word)`
Selecting by n
The graph above analyzes the most common positive and negative words used in inauguration speeches.
# 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
The table shows the bigrams most commonly used in inaugural speeches.
