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validateIt: Validating Topic Coherence and Topic Labels
By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.
| Version: |
1.2.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
pyMTurkR, rlang (≥ 0.4.11), tm (≥ 0.7-11), here, SnowballC |
| Suggests: |
roxygen2, testthat |
| Published: |
2023-05-16 |
| Author: |
Luwei Ying  [aut,
cre],
Jacob Montgomery [aut],
Brandon Stewart [aut] |
| Maintainer: |
Luwei Ying <triads.developers at wustl.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
validateIt results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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