Model for Semisupervised Text Analysis Based on Word Embeddings

A word embeddings-based semisupervised model for document scaling Watanabe (2020) . LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.


Reference manual

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1.0.2 by Kohei Watanabe, a month ago

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Authors: Kohei Watanabe [aut, cre, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports quanteda, quanteda.textstats, stringi, digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, stats, ggplot2, ggrepel, reshape2, locfit

Depends on methods

Suggests testthat

See at CRAN