Sentiment analysis is a popular technique in text mining. Roughly speaking, the technique is an attempt to determine the overall emotional attitude of a piece of text (i.e., positive or negative). We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a "dictionary", and then an sum of those scores for the document is produced. We use the 'Hu' and 'Liu' sentiment dictionary for determining sentiment. The scoring function is 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'.
| Version: | 0.1-1 |
| Depends: | R (≥ 3.0.0) |
| Published: | 2017-10-26 |
| Author: | Drew Schmidt [aut, cre] |
| Maintainer: | Drew Schmidt <wrathematics at gmail.com> |
| BugReports: | https://github.com/wrathematics/meanr/issues |
| License: | BSD 2-clause License + file LICENSE |
| URL: | https://github.com/wrathematics/meanr |
| NeedsCompilation: | yes |
| Citation: | meanr citation info |
| Materials: | README ChangeLog |
| CRAN checks: | meanr results |
| Reference manual: | meanr.pdf |
| Package source: | meanr_0.1-1.tar.gz |
| Windows binaries: | r-devel: meanr_0.1-1.zip, r-release: meanr_0.1-1.zip, r-oldrel: meanr_0.1-1.zip |
| OS X binaries: | r-release: meanr_0.1-1.tgz, r-oldrel: meanr_0.1-1.tgz |
| Old sources: | meanr archive |
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