model_lsi: Latent Semantic Indexing Model
In news-r/gensimr: Topic Modelling

Description Usage Arguments Details Examples

Transform into a latent n dimensional space via Latent Semantic Indexing.

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model_lsi(corpus, distributed = FALSE, ...)

load_lsi(file)

`corpus`	Corpus as returned by `wrap`. A tf-idf/bag-of-words transformation is recommended for LSI.
`distributed`	If `TRUE` - distributed mode (parallel execution on several machines) will be used.
`...`	Any other options, from the official documentation.
`file`	Path to a saved model.

Target dimensionality (num_topics) of 200–500 is recommended as a “golden standard” https://dl.acm.org/citation.cfm?id=1458105.

docs <- prepare_documents(corpus)
dictionary <- corpora_dictionary(docs)
corpora <- doc2bow(dictionary, docs)

# fit model
lsi <- model_lsi(corpora, id2word = dictionary, num_topics = 2L)
lsi$print_topics()