textTopicsReduce {text}

textTopicsReduce (EXPERIMENTAL)

Description

textTopicsReduce (EXPERIMENTAL)

Usage

textTopicsReduce(
  data,
  data_var,
  n_topics = 10,
  load_path = "./results",
  save_dir = "./results_reduced",
  embedding_model = "default"
)

Arguments

data

(tibble/data.frame) A tibble with a text-variable to be analysed, and optional numeric/categorical variables that you might want to use for later analyses testing the significance of topics in relation to these variables.

data_var

(string) Name of the text-variable in the data tibble that you want to perform topic modeling on.

n_topics

(string) The dimension reduction algorithm, currently only "default" is supported.

load_path

(string) The clustering algorithm to use, currently only "default" is supported.

save_dir

(string) The directory for saving results.

embedding_model

(string) Name of the embedding model to use such as "miniLM", "mpnet", "multi-mpnet", "distilroberta".

Value

A folder containing the model, data, folder with terms and values for each topic, and the document-topic matrix. Moreover the model itself is returned formatted as a data.frame together with metdata.

See Also

See textTopics textTopicsTest and textTopicsWordcloud.