fit_stm: Fit STM (Structural topic model)

Description

Estimate a structural topic model

Usage

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

Arguments

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.

Value

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

Details

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.

Examples

Run this code

# 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")
# }