You can access it from 'Add' (Plus) button. "Text Mining..." -> "Calculate TF-IDF".

Select a column as document id - A column considered as document id. If you run do_tokenize beforehand, this can be document_id.

Select a column that has tokenized text - Set a column that has tokens. This is "token" column if it's tokenized by do_tokenize function.

TF Weight (Optional) - The default is "raw".

"raw" is count of a term in a document.

"binary" is if it exists or not. If it exists, it is 1 and if not, it is 0.

"log_scale" is

`1+log(count of a term in a document)`

.

IDF Log Scale Function (Optional) - The default is log. This is a function to suppress the increase of idf value. Idf is calculated by

`log_scale_function((the total number of documents)/(the number of documents which have the token))`

. It's how rare the token is in the set of documents. It might be worth trying log2 or log10. log2 increases the value more easily and log10 increases it more slowly.Normalization (Optional) - The default is l2. How to normalize the tfidf vector.

"l2" is normalization that Euclidean distance of the tfidf vector for a document becomes 1.

"l1" is normalization that Manhattan distance (sum of values) of the tfidf vector for a document becomes 1.

FALSE doesn't normalize the result.