Representasi bag-of-words

Pengantar Natural Language Processing di R

Kasey Jones

Research Data Scientist

Contoh sebelumnya

animal_farm %>%
  unnest_tokens(output = "word", token = "words",
                input = text_column) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE)

# A tibble: 3.611 x 2
   word         n
   <chr>    <int>
 1 animals    248
 2 farm       163
 ...

Representasi bag-of-words

text1 <- c("Few words are important.")
text2 <- c("All words are important.")
text3 <- c("Most words are important.")

Kata unik:

few: hanya di text1
all: hanya di text2
most: hanya di text3
words, are, important

Representasi vektor umum

# Huruf kecil, tanpa stop words
word_vector <- c("few", "all", "most", "words", "important")

# Representasi untuk text1
text1 <- c("Few words are important.")
text1_vector <- c(1, 0, 0, 1, 1)
# Representasi untuk text2
text2 <- c("All words are important.")
text2_vector <- c(0, 1, 0, 1, 1)
# Representasi untuk text3
text3 <- c("Most words are important.")
text3_vector <- c(0, 0, 1, 1, 1)

Representasi tidytext

words <- animal_farm %>%
    unnest_tokens(output = "word", token = "words", input = text_column) %>%
    anti_join(stop_words) %>%
    count(chapter, word, sort = TRUE)
words

# A tibble: 6.807 x 3
   chapter    word         n
   <chr>      <chr>    <int>
 1 Chapter 8  napoleon    43
 2 Chapter 8  animals     41
 3 Chapter 9  boxer       34
...

Contoh satu kata

words %>%
  filter(word == 'napoleon') %>%
  arrange(desc(n))

# A tibble: 9 x 3
  chapter    word         n
  <chr>      <chr>    <int>
1 Chapter 8  napoleon    43
2 Chapter 7  napoleon    24
3 Chapter 5  napoleon    22
...
8 Chapter 3  napoleon     3
9 Chapter 4  napoleon     1

Matriks jarang (sparse)

library(tidytext); library(dplyr)
russian_tweets <- read.csv("russian_1.csv")
russian_tweets <- as_tibble(russian_tweets)

tidy_tweets <- russian_tweets %>%
  unnest_tokens(word, content) %>%
  anti_join(stop_words)
tidy_tweets %>%
  count(word, sort = TRUE)

# A tibble: 43,666 x 2
...

Lanjutan matriks jarang

Matriks jarang

20.000 baris (tweet)
43.000 kolom (kata)
20.000 × 43.000 = 860.000.000
Hanya 177.000 entri non-0. ≈0,02%

Contoh matriks jarang: Matriks jarang memiliki sangat sedikit entri non-0. Saat dilihat, kebanyakan berisi nol.

Latihan BoW

Pengantar Natural Language Processing di R