tidytext, quanteda, and tm returning different tf-idf scores

Question

I am trying to work on tf-idf weighted corpus (where I expect tf to be a proportion by document rather than simple count). I would expect the same values to be returned by all the classic text mining libraries, but I am getting different values. Is there an error in my code (e.g. do I need to transpose an object?) or do the default parameters of tf-idf counts differ accross the packages?

library(tm)
library(tidyverse) 
library(quanteda)
df <- as.data.frame(cbind(doc = c("doc1", "doc2"), text = c("the quick brown fox jumps over the lazy dog", "The quick brown foxy ox jumps over the lazy god")), stringsAsFactors = F)

df.count1 <- df %>% unnest_tokens(word, text) %>% 
  count(doc, word) %>% 
  bind_tf_idf(word, doc, n) %>% 
  select(doc, word, tf_idf) %>% 
  spread(word, tf_idf, fill = 0) 

df.count2 <- df %>% unnest_tokens(word, text) %>% 
  count(doc, word) %>% 
  cast_dtm(document = doc,term = word, value = n, weighting = weightTfIdf) %>% 
  as.matrix() %>% as.data.frame()

df.count3 <- df %>% unnest_tokens(word, text) %>% 
  count(doc, word) %>% 
  cast_dfm(document = doc,term = word, value = n) %>% 
  dfm_tfidf() %>% as.data.frame()

   > df.count1
# A tibble: 2 x 12
  doc   brown    dog    fox   foxy    god jumps  lazy  over     ox quick   the
  <chr> <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
1 doc1      0 0.0770 0.0770 0      0          0     0     0 0          0     0
2 doc2      0 0      0      0.0693 0.0693     0     0     0 0.0693     0     0

> df.count2
     brown       dog       fox jumps lazy over quick the foxy god  ox
doc1     0 0.1111111 0.1111111     0    0    0     0   0  0.0 0.0 0.0
doc2     0 0.0000000 0.0000000     0    0    0     0   0  0.1 0.1 0.1

> df.count3
     brown     dog     fox jumps lazy over quick the    foxy     god      ox
doc1     0 0.30103 0.30103     0    0    0     0   0 0.00000 0.00000 0.00000
doc2     0 0.00000 0.00000     0    0    0     0   0 0.30103 0.30103 0.30103

Mangosteen

Answer 1

You stumbled upon the differences in calculating the term frequencies.

Standard definitions:

TF: Term Frequency: TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).

IDF: Inverse Document Frequency: IDF(t) = log(Total number of documents / Number of documents with term t in it)

Tf-idf weight is the product of these quantities TF * IDF

Looks simple, but it isn't. Let's calculate the tf_idf for the word dog in doc1.

First TF for dog: That is 1 term / 9 terms in doc = 0.11111

1/9 = 0.1111111

Now IDF for dog: the log of (2 documents / 1 term). Now there are multiple possibilities, namely: log (or natural log), log2 or log10!

log(2) = 0.6931472
log2(2) = 1
log10(2) = 0.30103

#tf_idf on log:
1/9 * log(2) = 0.07701635

#tf_idf on log2:
1/9 * log2(2)  = 0.11111

#tf_idf on log10:
1/9 * log10(2) = 0.03344778

Now it gets interesting. Tidytext gives you a correct weighting based on log. tm returns the tf_idf based on log2. I expected the value 0.03344778 from quanteda because their base is log10.

But looking into quanteda, it returns the result correctly, but uses a count as default instead of a proportional count. To get everything as it should be, try the code as follows:

df.count3 <- df %>% unnest_tokens(word, text) %>% 
  count(doc, word) %>% 
  cast_dfm(document = doc,term = word, value = n)


dfm_tfidf(df.count3, scheme_tf = "prop", scheme_df = "inverse")
Document-feature matrix of: 2 documents, 11 features (22.7% sparse).
2 x 11 sparse Matrix of class "dfm"
      features
docs   brown        fox        god jumps lazy over quick the      dog     foxy       ox
  doc1     0 0.03344778 0.03344778     0    0    0     0   0 0        0        0       
  doc2     0 0          0              0    0    0     0   0 0.030103 0.030103 0.030103

That looks better and this is based on log10.

If you use quanteda with adjustments to the parameters, you can get the tidytext or tm outcome by changing the base parameter.

# same as tidytext the natural log
dfm_tfidf(df.count3, scheme_tf = "prop", scheme_df = "inverse", base = exp(1))

# same as tm
dfm_tfidf(df.count3, scheme_tf = "prop", scheme_df = "inverse", base = 2)

Filial

Thanks so much for such a thorough answer. I was hoping it was just an inconsistency of this sort. I really did spend many hours trying to figure it out on my own, but either the documentation in the packages was as sparse as my matrices or too cryptic for me to understand their default technique for counting tf-idf. – Mangosteen

Nice answer @phiver. In quanteda we use base 10 and count tf because that's how it's done in IIR. But the options exist to change this as the user wishes. Note: This can all be done in one (piped) operation as corpus(df) %>% dfm() %>% dfm_tfidf(). – Pantheas