TFIDF calculating confusion

问题

I found the following code on the internet for calculating TFIDF:

https://github.com/timtrueman/tf-idf/blob/master/tf-idf.py

I added "1+" in the function def idf(word, documentList) so i won't get divided by 0 error:

return math.log(len(documentList) / (1 + float(numDocsContaining(word,documentList))))

But i am confused for two things:

1. I get negative values in some cases, is this correct?
2. I am confused with line 62, 63 and 64.

Code:

 documentNumber = 0
  for word in documentList[documentNumber].split(None):
       words[word] = tfidf(word,documentList[documentNumber],documentList)

Should TFIDF be calculated on the first document only?

回答1:

1. No. Tf-idf is tf, a non-negative value, times idf, a non-negative value, so it can never be negative. This code seems to be implementing the erroneous definition of tf-idf that's been on the Wikipedia for years (it's been fixed in the meantime).

回答2:

If the word in question is contained in every document in the collection your 1+ change will result in a negative value. As 0 < (x / (1 + x)) < 1 holds for all x > 0. Which results in a negative logarithm.

In my opinion the correct IDF for a nonexistent word is infinite or undefined, but by adding 1+ to the denominator and the nominator a nonexistent word will have an IDF slightly higher than any existing word and words that exist in every document will have an IDF of zero. Both cases will probably work well with your code.

来源：https://stackoverflow.com/questions/16648599/tfidf-calculating-confusion