to_ldavis {Twitmo}

Create interactive visualization with LDAvis

Description

Converts LDA topic model to LDAvis compatible json string and starts server. May require servr Package to run properly. For conversion of STM topic models use toLDAvis.

Usage

to_ldavis(fitted, corpus, doc_term)

Arguments

fitted

Fitted LDA Model. Object of class LDA)

corpus

Document corpus. Object of class corpus)

doc_term

document term matrix (dtm).

Details

Beware that to_ldavis might fail if the corpus contains documents that consist ONLY of numbers, emojis or punctuation e.g. do not contain a single character string. This is due to a limitation in the topicmodels package used for model fitting that does not consider such terms as words and omits them causing the posterior to differ in length from the corpus. If you encounter such an error, redo your pre-processing and exclude emojis, punctuation and numbers. When using pool_tweets you can remove emojis by specifying remove_emojis = TRUE.

Value

Invisible Object (see serVis)).

See Also

toLDAvis

Examples

## Not run: 

library(Twitmo)

# load tweets (included in package)
mytweets <- load_tweets(system.file("extdata", "tweets_20191027-141233.json", package = "Twitmo"))

# Pool tweets into longer pseudo-documents
pool <- pool_tweets(data = mytweets)
pooled_dfm <- pool$document_term_matrix
pooled_corp <- pool$corpus

# fit your LDA model with 7 topics
model <- fit_lda(pooled_dfm, n_topics = 7, method = "Gibbs")

# Explore your topics with LDAvis
to_ldavis(model, pooled_corp, pooled_dfm)

## End(Not run)