oolong: Create Validation Tests for Automated Content Analysis

Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion and Topic intrusion tests, as described in Chang et al. (2009) <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.

Version:	0.3.4
Depends:	R (≥ 3.5)
Imports:	stm, purrr, tibble, shiny, miniUI, text2vec (≥ 0.6), digest, R6, quanteda, irr, ggplot2, cowplot, dplyr, stats, utils
Suggests:	testthat (≥ 2.1.0), topicmodels, covr, stringr, knitr, rmarkdown
Published:	2020-03-21
Author:	Chung-hong Chan [aut, cre]
Maintainer:	Chung-hong Chan <chainsawtiney at gmail.com>
BugReports:	https://github.com/chainsawriot/oolong/issues
License:	LGPL-2.1 | LGPL-3 [expanded from: LGPL (≥ 2.1)]
URL:	https://github.com/chainsawriot/oolong
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	oolong results

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