The bag-of-words representation

Introduction to Natural Language Processing in R

Kasey Jones

Research Data Scientist

The previous example

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
 ...

The bag-of-words representation

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

Unique Words:

few: only in text1
all: only in text2
most: only in text3
words, are, important

Typical vector representations

# Lowercase, without stop words
word_vector <- c("few", "all", "most", "words", "important")

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

tidytext representation

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
...

One word example

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

Sparse matrices

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
...

Sparse matrices continued

Sparse Matrix

20,000 rows (the tweets)
43,000 columns (the words)
20,000 * 43,000 = 860,000,000
Only 177,000 non-0 entries. About .02%

Sparse matrix example: Sparse matrices have very few non-0 entries in them. Viewing a sparse matrix just looks like a lot of zeros.

BoW Practice

Introduction to Natural Language Processing in R