R: Linear SVM classifier for texts

textmodel_svm {quanteda.textmodels} | R Documentation

## Linear SVM classifier for texts

### Description

Fit a fast linear SVM classifier for texts, using the LiblineaR package.

### Usage

    textmodel_svm(
      x,
      y,
      weight = c("uniform", "docfreq", "termfreq"),
      type = 1,
      ...
    )

### Arguments

`x` | the dfm on which the model will be fit. Does not need to contain only the training documents.
`y` | vector of training labels associated with each document identified in `train`. (These will be converted to factors if not already factors.)
`weight` | weights for different classes for imbalanced training sets, passed to `wi` in `LiblineaR::LiblineaR()`. `"uniform"` uses default; `"docfreq"` weights by the number of training examples, and `"termfreq"` by the relative sizes of the training classes in terms of their total lengths in tokens.
`type` | argument passed to the `type` argument in `LiblineaR::LiblineaR()`; default is `1` for L2-regularized L2-loss support vector classification (dual)
`...` | additional arguments passed to `LiblineaR::LiblineaR()`

### References

R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. (2008) LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9: 1871-1874. https://www.csie.ntu.edu.tw/~cjlin/liblinear/.

### See Also

`LiblineaR::LiblineaR()` `predict.textmodel_svm()`

### Examples

    # use party leaders for govt and opposition classes
    library("quanteda")
    docvars(data_corpus_irishbudget2010, "govtopp") <-
        c(rep(NA, 4), "Gov", "Opp", NA, "Opp", NA, NA, NA, NA, NA, NA)
    dfmat <- dfm(tokens(data_corpus_irishbudget2010))
    tmod <- textmodel_svm(dfmat, y = dfmat$govtopp)
    predict(tmod)

    # multiclass problem - all party leaders
    tmod2 <- textmodel_svm(dfmat,
        y = c(rep(NA, 3), "SF", "FF", "FG", NA, "LAB", NA, NA, "Green", rep(NA, 3)))
    predict(tmod2)

[Package quanteda.textmodels version 0.9.4 Index]