Model for Semisupervised Text Analysis Based on Word Embeddings

A word embeddings-based semisupervised models for document scaling Watanabe (2017) . LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).


Reference manual

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0.9.2 by Kohei Watanabe, 9 days ago

Browse source code at

Authors: Kohei Watanabe [aut, cre, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, grDevices, stats, ggplot2, ggrepel, reshape2, e1071

Depends on quanteda, quanteda.textmodels, methods

Suggests testthat

See at CRAN