Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
caret, compiler, data.table, foreach, ggplot2, glmnet, ISOweek, quanteda, Rcpp (≥ 0.12.13), RcppRoll, RcppParallel, stats, stringi, utils |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppParallel |
| Suggests: |
covr, doParallel, e1071, lexicon, MCS, NLP, parallel, randomForest, stopwords, testthat, tm |
| Published: |
2021-08-18 |
| DOI: |
10.32614/CRAN.package.sentometrics |
| Author: |
Samuel Borms 
[aut, cre],
David Ardia [aut],
Keven Bluteau
[aut],
Kris Boudt [aut],
Jeroen Van Pelt [ctb],
Andres Algaba [ctb] |
| Maintainer: |
Samuel Borms <borms_sam at hotmail.com> |
| BugReports: |
https://github.com/SentometricsResearch/sentometrics/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://sentometrics-research.com/sentometrics/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Citation: |
sentometrics citation info |
| Materials: |
README NEWS |
| In views: |
NaturalLanguageProcessing |
| CRAN checks: |
sentometrics results |