# compute tf-idf weights from a dfm

### Description

Compute tf-idf, inverse document frequency, and relative term frequency on
document-feature matrices. See also `weight`

.

### Usage

1 2 3 4 |

### Arguments

`x` |
object for which idf or tf-idf will be computed (a document-feature matrix) |

`...` |
additional arguments passed to |

`normalize` |
if |

`scheme` |
scheme for |

### Details

`tfidf`

computes term frequency-inverse document frequency
weighting. The default is not to normalize term frequency (by computing
relative term frequency within document) but this will be performed if
`normalize = TRUE`

.

### References

Manning, C. D., Raghavan, P., & Schutze, H. (2008).
*Introduction to Information Retrieval*. Cambridge University Press.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
head(LBGexample[, 5:10])
head(tfidf(LBGexample)[, 5:10])
docfreq(LBGexample)[5:15]
head(tf(LBGexample)[, 5:10])
# replication of worked example from
# https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
(wikiDfm <- new("dfmSparse",
Matrix::Matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
byrow = TRUE, nrow = 2,
dimnames = list(docs = c("document1", "document2"),
features = c("this", "is", "a", "sample", "another",
"example")), sparse = TRUE)))
docfreq(wikiDfm)
tfidf(wikiDfm)
``` |