number_topics: Number Topics

In evan-l-munson/SAoTD: Sentiment Analysis of Twitter Data

Number Topics

Description

Determines the optimal number of Latent topics within a data frame by tuning the Latent Dirichlet Allocation (LDA) model parameters. Uses the 'ldatuning' package and outputs an ldatuning plot. __This process can be time consuming depending on the size of the input data frame.__

Usage

number_topics(
  DataFrame,
  num_cores = 1L,
  min_clusters = 2,
  max_clusters = 12,
  skip = 2,
  set_seed = 1234
)

Arguments

DataFrame	Data Frame of Twitter Data.
num_cores	The number of CPU cores to processes models simultaneously (2L for dual core processor).
min_clusters	Lower range for the number of clusters.
max_clusters	Upper range for the number of clusters.
skip	Integer; The number of clusters to skip between entries.
set_seed	Seed for reproducible results.

Value

A Tidy DataFrame.

Examples

## Not run: 
library(saotd)
data <- raw_tweets
LDA_Topic_Plot <- number_topics(DataFrame = data,
                                num_cores = 2L,
                                min_clusters = 2,
                                max_clusters = 12, 
                                skip = 2,
                                set_seed = 1234)

LDA_Topic_Plot 

## End(Not run)

evan-l-munson/SAoTD documentation built on Aug. 12, 2024, 1:08 a.m.