USING TFIDF FOR RELATIVE FREQUENCY, COSINE SIMILARITY

Question

I'm trying to use TFIDF for relative frequency to calculate cosine distance. I've selected 10 words from one document say: File 1 and selected another 10 files from my folder, using the 10 words and their frequency to check which of the 10 files are similar to File 1. Say Total number of files in folder are 46.i know that DF(is the no of documents the word appears in) IDF(is log(total no of files(46)/DF) and TFIDF(is the product of TF(frequency of the word in one doc) and IDF)

QUESTION:

1. Assuming what i said above is 100% correct, after getting the TFIDF for all 10 words in one document say: File 2, Do i add all the TFIDF for each of the 10 words together to get the TFIDF for File 2?

2. What is the cosine distance?

Could anyone help with an example?

Answer 1

The problem is you are confused between cosine similarity and tf-idf. While the former is a measure of similarity between two vectors (in this case documents), the latter simply is a technique of setting the components for the vectors to be eventually used in the former.

Particular to your question, it is rather inconvenient to select 10 terms from each document. I'd rather suggest to work with all terms. Let V be the total number of terms (the cardinality of the set of union over all documents in the collection). You can the represent each document as a vector of V dimensions. The ith component of a particular document D can be set to the tf-idf weight corresponding to that term (say t), i.e. D_i = tf(t,D)*idf(t)

Once you represent every document in your collection in this way, you can then compute the inter-document similarities in the following way.

cosine-sim(D, D') = (1/|D_1|*|D'|) * \sum_{i=1}^{V} D_i * D'_i
                  = (1/|D_1|*|D'|) * \sum_{i=1}^{V} tf(t,D)*idf(t)*tf(t,D')*idf(t)  

Note that the contributing terms in this summation are only those ones which occur in both documents. If a term t occurs in D but not in D' then tf(t,D')=0 which thus contributes 0 to the sum.