tf_idf: find the most relevant document for a queery by computing...
In till-tietz/rbow: Bag of Words and Document Frequency Analysis for Multiple Dictionaries
Description
Usage
Arguments
Value
Description
find the most relevant document for a queery by computing tf-idf for a set of terms within a corpus
Usage
1
Arguments
corpus
the text or texts to be analyzed as a list of character vectors
terms
a character vector (or a regular expression if own_regex == TRUE) of terms to determine document relevance with
own_regex
when TRUE allows you to add custom regular expressions for terms argument. when FALSE rbow will construct regular expression from the character vector you supplied. defaults to FALSE
Value
a data frame with document number and mean tf-idf
till-tietz/rbow documentation built on Oct. 21, 2021, 9:16 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value
Description
find the most relevant document for a queery by computing tf-idf for a set of terms within a corpus
Usage
1 |
Arguments
corpus |
the text or texts to be analyzed as a list of character vectors |
terms |
a character vector (or a regular expression if own_regex == TRUE) of terms to determine document relevance with |
own_regex |
when TRUE allows you to add custom regular expressions for terms argument. when FALSE rbow will construct regular expression from the character vector you supplied. defaults to FALSE |
Value
a data frame with document number and mean tf-idf
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.