Manifestos analysis
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.1.0 ✔ purrr 0.2.5
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## ✖ dplyr::filter() masks stats::filter()
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library(tidytext) # package for text analysis
library(readxl) # reads excel files, the format I used for the dataRead files
manifestos <- read_excel("manifestos.xlsx")
manifestosPrepare the text
manifesto_words <- manifestos %>%
unnest_tokens(word, text)Counts of manifesto words
manifesto_words %>%
group_by(author) %>%
summarize(num_words = n(),
lex_diversity = n_distinct(word),
lex_density = lex_diversity/num_words)Mean word length
manifesto_words %>%
group_by(author) %>%
mutate(word_length = nchar(word)) %>%
distinct(word, word_length, author) %>%
arrange(-word_length)Histogram for manifesto word length by author
manifesto_words %>%
mutate(word_length = nchar(word)) %>%
ggplot(aes(word_length, fill = author)) +
geom_histogram(binwidth = 1, show.legend = F) +
facet_wrap(vars(author), scales = "free_y") +
labs(title = "word length distributions of manifestos, by author") +
theme_minimal() +
scale_fill_viridis_d()
Get stop words
stopwords <- get_stopwords()Histogram of word choice and the number of times each were used, by author
manifesto_words %>%
anti_join(stop_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()## Joining, by = "word"## Selecting by n
Calculate tf-idf
manifesto_word_counts <- manifestos %>% # This counts each word per author
unnest_tokens(word, text) %>%
count(author, word, sort = TRUE)
total_words <- manifesto_word_counts %>% # This counts total words per author
group_by(author) %>%
summarize(total = sum(n))
manifesto_word_counts <- left_join(manifesto_word_counts, total_words) # Joins the two## Joining, by = "author"manifesto_tf_idf <- manifesto_word_counts %>% # Calculates tf-idf
bind_tf_idf(word, author, n)
manifesto_tf_idf %>% # Displays it
arrange(-tf_idf) Graph manifesto
manifesto_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
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