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: | 1.0.1 |
| 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: | 2025-04-03 |
| 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 |