`dfm_tfidf.Rd`

Weight a dfm by term frequency-inverse document frequency (*tf-idf*),
with full control over options. Uses fully sparse methods for efficiency.

dfm_tfidf(x, scheme_tf = "count", scheme_df = "inverse", base = 10, force = FALSE, ...)

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

scheme_tf | scheme for |

scheme_df | scheme for |

base | the base for the logarithms in the |

force | logical; if |

... | additional arguments passed to |

`dfm_tfidf`

computes term frequency-inverse document frequency
weighting. The default is to use counts instead of normalized term
frequency (the relative term frequency within document), but this
can be overridden using `scheme_tf = "prop"`

.

Manning, C. D., Raghavan, P., & Schütze, H. (2008).
*Introduction to Information Retrieval*. Cambridge: Cambridge University Press.
https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> 6 x 6 sparse Matrix of class "dfm" #> features #> docs E F G H I J #> R1 45 78 115 146 158 146 #> R2 0 2 3 10 22 45 #> R3 0 0 0 0 0 0 #> R4 0 0 0 0 0 0 #> R5 0 0 0 0 0 0 #> V1 0 0 0 2 3 10#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> 6 x 6 sparse Matrix of class "dfm" #> features #> docs E F G H I J #> R1 35.01681 37.2154579 54.868944 43.95038 47.56274 43.95038 #> R2 0 0.9542425 1.431364 3.01030 6.62266 13.54635 #> R3 0 0 0 0 0 0 #> R4 0 0 0 0 0 0 #> R5 0 0 0 0 0 0 #> V1 0 0 0 0.60206 0.90309 3.01030#> E F G H I J K L M N O #> 1 2 2 3 3 3 4 4 4 4 4#> Document-feature matrix of: 6 documents, 6 features (61.1% sparse). #> 6 x 6 sparse Matrix of class "dfm" #> features #> docs E F G H I J #> R1 45 78 115 146 158 146 #> R2 0 2 3 10 22 45 #> R3 0 0 0 0 0 0 #> R4 0 0 0 0 0 0 #> R5 0 0 0 0 0 0 #> V1 0 0 0 2 3 10# replication of worked example from # https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf dfmat2 <- 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"))) %>% as.dfm() dfmat2#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs this is a sample another example #> document1 1 1 2 1 0 0 #> document2 1 1 0 0 2 3docfreq(dfmat2)#> this is a sample another example #> 2 2 1 1 1 1#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs this is a sample another example #> document1 0 0 0.12 0.06 0 0 #> document2 0 0 0 0 0.09 0.13# NOT RUN { # comparison with tm if (requireNamespace("tm")) { convert(dfmat2, to = "tm") %>% tm::weightTfIdf() %>% as.matrix() # same as: dfm_tfidf(dfmat2, base = 2, scheme_tf = "prop") } # }