Generate pairwise correlations for a vector of terms of interest.

Usage

calculate_corr(
 df,
 text_var,
 terms,
 min_freq = 10,
 corr_limits = c(-1, 1),
 n_corr = 75,
 hashtags = FALSE,
 mentions = FALSE,
 clean_text = FALSE
)

Arguments

df: A dataframe where each row is a separate post.
text_var: The variable containing the text which you want to explore.
terms: The terms of interest. You can use multi-word phrases.
min_freq: The minimum number of times a term must be observed to be considered.
corr_limits: Numerical lower and upper bounds for correlations.
n_corr: The number of correlations to include (begins with the most positive within the range specified in corr_limits).
hashtags: Should hashtags be included?
mentions: Should mentions be included?
clean_text: Should the text variable be cleaned?

Value

A list containing a summary table and a tidygraph object suitable for a network visualisation.

Examples

{spinklr_export <- ParseR::sprinklr_export
x <- calculate_corr(
  df = sprinklr_export,
  text_var = Message,
  terms = c("bobby", "trump"),
  min_freq = 10,
  corr_limits = c(-1, 1),
  n_corr = 75,
  hashtags = TRUE,
  mentions = FALSE
)
viz_corr(x$viz)
}