predict_lda: Predict topics of tweets using fitted LDA model

In abuchmueller/Twitmo: Twitter Topic Modeling and Visualization for R

Predict topics of tweets using fitted LDA model

Description

Predict topics of tweets using fitted LDA model.

Usage

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

Arguments

data	Data frame containing tweets and hashtags. Works with any data frame, as long as there is a "text" column of type character string and a "hashtags" column with comma separated character vectors. Can be obtained either by using load_tweets on a json object returned by Twitter's API v1.1 or by using stream_in on any json file, as long as it has a "text" and "hashtags" field. If you are unsure about the requirements you may load the sample piece of data contained in the package by following the example in the the example section of this help page.
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).

Value

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

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

# 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)

abuchmueller/Twitmo documentation built on Sept. 14, 2022, 8:06 p.m.