predict_lda: Predict topics of tweets using fitted LDA model
In Twitmo: Twitter Topic Modeling and Visualization for R

Description Usage Arguments Value Examples

Predict topics of tweets using fitted LDA model.

predict_lda(
  data,
  lda_model,
  response = "max",
  remove_numbers = TRUE,
  remove_punct = TRUE,
  remove_symbols = TRUE,
  remove_url = TRUE
)

`data`	Data frame of parsed tweets. Obtained either by using `load_tweets` or `stream_in` in conjunction with `tweets_with_users`.
`lda_model`	Fitted LDA Model. Object of class LDA.
`response`	Type of response. Either "prob" for probabilities or "max" one topic (default).
`remove_numbers`	Logical. If `TRUE` remove tokens that consist only of numbers, but not words that start with digits, e.g. 2day. See tokens.
`remove_punct`	Logical. If `TRUE` remove all characters in the Unicode "Punctuation" [P] class, with exceptions for those used as prefixes for valid social media tags if `preserve_tags = TRUE`. See tokens
`remove_symbols`	Logical. If `TRUE` remove all characters in the Unicode "Symbol" [S] class.
`remove_url`	Logical. If `TRUE` find and eliminate URLs beginning with http(s).

Data frame of topic predictions or predicted probabilities per topic (see response).

## 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

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

# Predict topics of tweets using your fitted LDA model
predict_lda(mytweets, model, response = "prob")

## End(Not run)