fit_stm: Fit STM (Structural topic model)
In Twitmo: Twitter Topic Modeling and Visualization for R

Description Usage Arguments Details Value See Also Examples

Estimate a structural topic model

fit_stm(
  data,
  n_topics = 2L,
  xcov,
  remove_punct = TRUE,
  stem = TRUE,
  remove_url = TRUE,
  remove_emojis = TRUE,
  stopwords = "en",
  ...
)

`data`	Data frame of parsed tweets. Obtained either by using `load_tweets` or `stream_in` in conjunction with `tweets_with_users`.
`n_topics`	Integer with number of topics.
`xcov`	Either a \[stats]formula with an empty left-hand side specifying external covariates (meta data) to use.e.g. `~favourites_count + retweet_count` or a character vector (`c("favourites_count", "retweet_count")`) or comma seperated character string (`"favourites_count,retweet_count"`) with column names implying which metadata to use as external covariates.
`remove_punct`	Logical. Indicates whether punctuation (includes Twitter hashtags and usernames) should be removed. Defaults to TRUE.
`stem`	Logical. If `TRUE` turn on word stemming for terms.
`remove_url`	Logical. If `TRUE` find and eliminate URLs beginning with http(s).
`remove_emojis`	Logical. If `TRUE` all emojis will be removed from tweets.
`stopwords`	a character vector, list of character vectors, dictionary or collocations object. See pattern for details. Defaults to stopwords("english").
`...`	Additional arguments passed to stm.

Use this to function estimate a STM from a data frame of parsed Tweets. Works with unpooled Tweets only. Pre-processing and fitting is done in one run.

Object of class stm. Additionally, pre-processed documents are appended into a named list called "prep".

stm

## Not run: 

library(Twitmo)

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

# fit STM with tweets
stm_model <- fit_stm(mytweets, n_topics = 7,
                     xcov = ~ retweet_count + followers_count + reply_count +
                     quote_count + favorite_count,
                     remove_punct = TRUE,
                     remove_url = TRUE,
                     remove_emojis = TRUE,
                     stem = TRUE,
                     stopwords = "en")

## End(Not run)