validateLabel: Create validation tasks for labels assigned to the topics in the topic model of choice.

Description

Create validation tasks for labels assigned to the topics in the topic model of choice.

Usage

validateLabel(
  type,
  n,
  text.predict = NULL,
  text.name = "text",
  top1.name = "top1",
  top2.name = "top2",
  top3.name = "top3",
  labels = NULL,
  labels.index = NULL,
  labels.add = NULL
)

Value

A matrix containing the validation tasks. The matrix has six value columns:

topic: The topic index associated with the document.
doc: The text of the document.
opt1: The first option label presented to the user.
opt2: The second option label presented to the user.
opt3: The third option label presented to the user.
optcrt: The correct label for the document.

Arguments

type

Task structures to be specified. Must be one of "LI" (Label Intrusion) and "OL" (Optimal Label).

n

The number of desired tasks

text.predict

A data frame or matrix containing both the text and the indicator(s) of the model predicted topic(s).

text.name

variable name in `text.predict` that indicates the text

top1.name

variable name in `text.predict` that indicates the top1 model predicted topic

top2.name

variable name in `text.predict` that indicates the top2 model predicted topic

top3.name

variable name in `text.predict` that indicates the top3 model predicted topic

labels

The user-defined labels assigned to the topics

labels.index

The topic index in correspondence with the labels, e.g., c(10, 12, 15). Must be in the same length and order with `label`.

labels.add

Labels from other broad catagories. Default to NULL. Users could specify them to evaluate how well different broad categories are distinguished from one another.

#' value A matrix containing the validation tasks as described in the return section.

Details

Users need to pick a topic model that they deem to be good and label the topics they later would like to use as measures.