textmodel_lsa {quanteda.textmodels}

Latent Semantic Analysis

Description

Fit the Latent Semantic Analysis scaling model to a dfm, which may be weighted (for instance using quanteda::dfm_tfidf()).

Usage

textmodel_lsa(x, nd = 10, margin = c("both", "documents", "features"))

Arguments

x

the dfm on which the model will be fit

nd

the number of dimensions to be included in output

margin

margin to be smoothed by the SVD

Details

svds in the RSpectra package is applied to enable the fast computation of the SVD.

Value

a textmodel_lsa class object, a list containing:

• sk a numeric vector containing the d values from the SVD

• docs document coordinates from the SVD (u)

• features feature coordinates from the SVD (v)

• matrix_low_rank the multiplication of udv'

• data the input data as a CSparseMatrix from the Matrix package

Note

The number of dimensions nd retained in LSA is an empirical issue. While a reduction in k can remove much of the noise, keeping too few dimensions or factors may lose important information.

Author(s)

Haiyan Wang and Kohei Watanabe

References

Rosario, B. (2000). Latent Semantic Indexing: An Overview. Technical report INFOSYS 240 Spring Paper, University of California, Berkeley.

Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6): 391.

See Also

predict.textmodel_lsa(), coef.textmodel_lsa()

Examples

library("quanteda")
dfmat <- dfm(tokens(data_corpus_irishbudget2010))
# create an LSA space and return its truncated representation in the low-rank space
tmod <- textmodel_lsa(dfmat[1:10, ])
head(tmod$docs)

# matrix in low_rank LSA space
tmod$matrix_low_rank[,1:5]

# fold queries into the space generated by dfmat[1:10,]
# and return its truncated versions of its representation in the new low-rank space
pred <- predict(tmod, newdata = dfmat[11:14, ])
pred$docs_newspace