tweet_topics {saotd}

Tweet Topics

Description

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.

Usage

tweet_topics(
  DataFrame,
  clusters,
  method = "Gibbs",
  num_terms = 10,
  set_seed = 1234
)

Arguments

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.

Value

Returns LDA topics.

Examples

## 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)