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3_Text_als_Daten.R
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library(friends)
library(dplyr)
library(tidytext)
library(wordcloud)
library(reshape2)
library(word2vec)
library(harrypotter)
library(uwot)
library(plotly)
library(doc2vec)
dplyr::glimpse(friends)
friends_data <- friends::friends
friends_data %>% group_by(season, episode, scene) %>%
unnest_tokens(word, text) %>% count(word) %>% summarise(word_per_scene = sum(n)) %>%
plot_ly( type = 'scatter', mode = 'lines', color = I("blue"))%>%
add_trace( y = ~word_per_scene, name = 'word_per_scene')%>%
layout(showlegend = F) %>%
layout(yaxis = list(title = 'Count'))
friends_data %>%
filter(speaker == "Monica Geller" | speaker == "Joey Tribbiani" | speaker == "Chandler Bing" | speaker == "Phoebe Buffay" | speaker == 'Rachel Green'
| speaker =="Ross Geller") %>%
group_by(speaker) %>%
unnest_tokens(word, text) %>%
count(word) %>%
arrange(desc(n), .by_group = T) %>%
acast(word ~ speaker, value.var = "n", fill = 0) %>%
comparison.cloud( title.size = 1, random.order = FALSE, max.words = 500)
#devtools::install_github("bradleyboehmke/harrypotter")
titles <- c("1.Philosopher's Stone", "2.Chamber of Secrets", "3.Prisoner of Azkaban",
"4.Goblet of Fire", "5.Order of the Phoenix", "6.Half-Blood Prince",
"7.Deathly Hallows")
books <- list(philosophers_stone, chamber_of_secrets, prisoner_of_azkaban,
goblet_of_fire, order_of_the_phoenix, half_blood_prince,
deathly_hallows)
harry_data <- tibble()
for ( i in seq_along(books)){
book <- tibble(title = titles[i],text = toString(books[[i]]))
harry_data <- rbind(harry_data, book)
}
harry_data %>% group_by(title) %>%
unnest_tokens(word, text) %>% filter(word == 'fear') %>% count(word) %>%
plot_ly( type = 'bar', color = I("blue"))%>%
add_trace(x = ~title, y = ~n, name = 'Count fear')%>%
layout(showlegend = F) %>%
layout(yaxis = list(title = 'Count'))
harry_data %>% group_by(title) %>%
unnest_tokens(word, text) %>%
count(word) %>%
arrange(desc(n), .by_group = T) %>%
acast(word ~ title, value.var = "n", fill = 0) %>%
comparison.cloud( title.size = 1, random.order = T,max.words=400,scale = c(2, 0.5))
# n-gramm
harry_data %>% group_by(title) %>%
unnest_tokens(bigram,text,token = "ngrams", n = 2 ) %>%
count(bigram) %>%
arrange(desc(n), .by_group = T) %>%
acast(bigram ~ title, value.var = "n", fill = 0) %>%
comparison.cloud( title.size = 1, random.order = T,max.words=200,scale = c(2, 0.5))
# tf–idf
harry_data %>% unnest_tokens(word, text) %>% group_by(title) %>%
count(word) %>%
bind_tf_idf(word, title, n) %>%
acast(word ~ title, value.var = "tf_idf", fill = 0) %>%
comparison.cloud( title.size = 1, random.order = T,max.words=200,scale = c(2, 0.5),)
# word2vec
set.seed(123456789)
vec_harry <- sapply(harry_data$text, paste, collapse=".")
model <- word2vec(x = vec_harry, type = "cbow", dim = 300, iter = 50, threads = 4)
embedding <- as.matrix(model)
labels <- harry_data$title
harry_2d <- umap(embedding, n_neighbors = 15, n_threads = 4)
harry_2d <- as.data.frame(harry_2d)
names(harry_2d) <- c("x1", "x2")
fig <- plot_ly(harry_2d, x = ~x1, y = ~x2, type = 'scatter', mode = 'markers', text = rownames(embedding) ) %>%
layout(plot_bgcolor = "#e5ecf6",
xaxis = list(
title = "0"),
yaxis = list(
title = "1"))
fig
harry_2d %>% filter(x1<0 -4)
# Friends doc2vec
friends_scene <- friends_data %>% group_by(scene, episode, season) %>% summarise(full_scene = paste0(text, collapse = "")) %>% arrange(season, episode, scene)
friends_data$doc_id <- gsub(" ", paste0(friends_data$speaker, friends_data$season, friends_data$episode, friends_data$scene, friends_data$utterance),replacement = "")
model <- paragraph2vec(x = friends_data, type = "PV-DBOW", dim = 300, iter = 50)
embedding <- as.matrix(model)
friends_2d <- umap(embedding, n_neighbors = 15, n_threads = 4)
friends_2d <- as.data.frame(friends_2d)
friends_2d <-tibble::rownames_to_column(friends_2d)
names(friends_2d)<- c("doc_id","x1", "x2")
friends_data <- friends_data %>% inner_join(friends_2d, friends_data, by= c("doc_id"= "doc_id"))
data <- friends_data%>%
filter(speaker == "Monica Geller" | speaker == "Joey Tribbiani" | speaker == "Chandler Bing" | speaker == "Phoebe Buffay"
| speaker =="Ross Geller")
fig <- plot_ly(data , x = ~x1.y, y = ~x2.y, type = 'scatter', mode = 'markers', color = data$season, text = data$speaker) %>%
layout(plot_bgcolor = "#e5ecf6",
xaxis = list(
title = "0"),
yaxis = list(
title = "1"))
fig
# By scene
friends_scene <- friends_data %>% group_by(scene, episode, season) %>% summarise(text = paste0(text, collapse = "")) %>% arrange(season, episode, scene)
friends_scene$doc_id <- gsub(" ", paste0(friends_scene$season, friends_scene$episode, friends_scene$scene),replacement = "")
model <- paragraph2vec(x = friends_scene, type = "PV-DBOW", dim = 300, iter = 50)
embedding <- as.matrix(model)
friends_2d <- umap(embedding, n_neighbors = 15, n_threads = 4)
friends_2d <- as.data.frame(friends_2d)
friends_2d <-tibble::rownames_to_column(friends_2d)
names(friends_2d)<- c("doc_id","x1", "x2")
friends_scene <-friends_scene %>% inner_join(friends_2d, friends_scene, by= c("doc_id"= "doc_id"))
fig <- plot_ly(friends_scene, x = ~x1, y = ~x2, type = 'scatter', mode = 'markers', color = friends_scene$season, text = friends_scene$season) %>%
layout(plot_bgcolor = "#e5ecf6",
xaxis = list(
title = "0"),
yaxis = list(
title = "1"))
fig