tweet_topics | R Documentation |
Determines the Latent topics within a data frame by using Latent Dirichlet Allocation (LDA) model parameters. Uses the 'ldatuning' package and outputs an ldatuning plot. Prepares Tweet text, creates DTM, conducts LDA, display data terms associated with each topic.
tweet_topics(
DataFrame,
clusters,
method = "Gibbs",
num_terms = 10,
set_seed = 1234
)
DataFrame |
Data Frame of Twitter Data. |
clusters |
The number of latent clusters. |
method |
method = "Gibbs" |
num_terms |
The desired number of terms to be returned for each topic. |
set_seed |
Seed for reproducible results. |
Returns LDA topics.
## Not run:
library(saotd)
data <- raw_tweets
LDA_data <- tweet_topics(DataFrame = data,
clusters = 8,
method = "Gibbs",
set_seed = 1234,
num_terms = 10)
LDA_data
## End(Not run)
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