sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

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. (2020) <doi:10.2139/ssrn.3067734>.

Version: 0.8.1
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, NLP, parallel, randomForest, testthat, tm
Published: 2020-03-11
Author: Samuel Borms ORCID iD [aut, cre], David Ardia ORCID iD [aut], Keven Bluteau ORCID iD [aut], Kris Boudt ORCID iD [aut], Jeroen Van Pelt [ctb], Andres Algaba [ctb]
Maintainer: Samuel Borms <samuel.borms at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: sentometrics citation info
Materials: README NEWS
CRAN checks: sentometrics results


Reference manual: sentometrics.pdf
Package source: sentometrics_0.8.1.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: sentometrics_0.8.1.tgz, r-oldrel: sentometrics_0.8.1.tgz
Old sources: sentometrics archive


Please use the canonical form to link to this page.