Helps in initial processing like converting text to lower case, removing punctuation, numbers, stop words, stemming, sparsity control and term frequency inverse document frequency processing. Helps in recognizing domain or corpus specific stop words. Makes use of 'ldatunig' output to pick optimal number of topics for topic modelling. Helps in topic modelling the content.
| Version: | 0.2.0 |
| Depends: | tm, NLP, topicmodels, Matrix, slam |
| Published: | 2017-12-21 |
| Author: | Krishna Harsha |
| Maintainer: | Krishna Harsha <khkrishnaharsha123 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | BullsEyeR results |
| Reference manual: | BullsEyeR.pdf |
| Package source: | BullsEyeR_0.2.0.tar.gz |
| Windows binaries: | r-devel: BullsEyeR_0.2.0.zip, r-release: BullsEyeR_0.2.0.zip, r-oldrel: BullsEyeR_0.2.0.zip |
| macOS binaries: | r-release: BullsEyeR_0.2.0.tgz, r-oldrel: BullsEyeR_0.2.0.tgz |
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