The TFIDF

Introduction to Natural Language Processing in R

Kasey Jones

Research Data Scientist

Bag-of-word pitfalls

t1 <- "My name is John. My best friend is Joe. We like tacos."
t2 <- "Two common best friend names are John and Joe."
t3 <- "Tacos are my favorite food. I eat them with my buddy Joe."

clean_t1 <- "john friend joe tacos"
clean_t2 <- "common friend john joe names"
clean_t3 <- "tacos favorite food eat buddy joe"

Sharing common words

clean_t1 <- "john friend joe tacos"
clean_t2 <- "common friend john joe names"
clean_t3 <- "tacos favorite food eat buddy joe"

Compare t1 and t2

3/4 words from t1 are in t2
3/5 words from t2 are in t1

Compare t1 and t3

2/4 words from t1 are in t3
2/6 words from t3 are in t1

Tacos matter

t1 <- "My name is John. My best friend is Joe. We like tacos."
t2 <- "Two common best friend names are John and Joe."
t3 <- "Tacos are my favorite food. I eat them with my friend Joe."

Words in each text:

John: t1, t2
Joe: t1, t2, t3
Tacos: t1, t3

TFIDF

clean_t1 <- "john friend joe tacos"
clean_t2 <- "common friend john joe names"
clean_t3 <- "tacos favorite food eat buddy joe"

TF: Term Frequency
- The proportion of words in a text that are that term
- john is 1/4 words in clean_t1, tf = .25
IDF: Inverse Document Frequency
- The weight of how common a term is across all documents
- john is in 3/3 documents, IDF = 0

IDF Equation

$ IDF = log \frac{N}{n_{t}} $

N: total number of documents in the corpus
$n_{t}$: number of documents where the term appears

Example:

Taco IDF: $log (\frac{3}{2}) = .405$
Buddy IDF: $log (\frac{3}{1}) = 1.10$
John IDF: $log (\frac{3}{3}) = 0$

TF + IDF

clean_t1 <- "john friend joe tacos"
clean_t2 <- "common friend john joe names"
clean_t3 <- "tacos favorite food eat buddy joe"

TFIDF for "tacos":

clean_t1: TF * IDF = (1/4) * (.405) = 0.101
clean_t2: TF * IDF = (0/4) * (.405) = 0
clean_t3: TF * IDF = (1/6) * (.405) = 0.068

Calculating the TFIDF matrix

# Create a data.frame
df <- data.frame('text' = c(t1, t2, t3), 'ID' = c(1, 2, 3))

df %>%
  unnest_tokens(output = "word", token = "words", input = text) %>%
  anti_join(stop_words) %>%
  count(ID, word, sort = TRUE) %>%
  bind_tf_idf(word, ID, n)

word: the column containing the terms
ID: the column containing document IDs
n: the word count produced by count()

bind_tf_idf output

# A tibble: 15 x 6
       X word         n    tf   idf tf_idf
   <dbl> <chr>    <int> <dbl> <dbl>  <dbl>
 1     1 friend       1 0.25  0.405 0.101 
 2     1 joe          1 0.25  0     0     
 3     1 john         1 0.25  0.405 0.101 
 4     1 tacos        1 0.25  0.405 0.101 
 5     2 common       1 0.2   1.10  0.220 
 6     2 friend       1 0.2   0.405 0.0811
 ...

TFIDF Practice

Introduction to Natural Language Processing in R