tm_cooc: Analyse word co-occurrence in subject lines and return a...

In vivainsights: Analyze and Visualize Data from 'Microsoft Viva Insights'

Analyse word co-occurrence in subject lines and return a network plot

Description

This function generates a word co-occurrence network plot, with options to return a table. This function is used within meeting_tm_report().

Usage

tm_cooc(data, stopwords = NULL, seed = 100, return = "plot", lmult = 0.05)

Arguments

data	A Meeting Query dataset in the form of a data frame.
stopwords	A character vector OR a single-column data frame labelled 'word' containing custom stopwords to remove.
seed	A numeric vector to set seed for random generation.
return	String specifying what to return. This must be one of the following strings: "plot" "table" See Value for more information.
lmult	A multiplier to adjust the line width in the output plot. Defaults to 0.05.

Details

This function uses tm_clean() as the underlying data wrangling function. There is an option to remove stopwords by passing a data frame into the stopwords argument.

Value

A different output is returned depending on the value passed to the return argument:

• "plot": 'ggplot' and 'ggraph' object. A network plot.

• "table": data frame. A summary table.

Example

The function can be run with subject lines from mt_data, as per below.

mt_data %>%
  tm_cooc(lmult = 0.01)

Author(s)

Carlos Morales [e-mail]

See Also

Other Text-mining: meeting_tm_report(), pairwise_count(), tm_clean(), tm_freq(), tm_wordcloud()

Examples

# Demo using a subset of `mt_data`

vivainsights documentation built on April 3, 2025, 9:25 p.m.