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. (2017) <doi:10.2139/ssrn.3067734>.

Version: 0.5.1
Depends: R (≥ 3.3.0), data.table
Imports: abind, caret, compiler, foreach, ggplot2, glmnet, ISOweek, quanteda, Rcpp (≥ 0.12.13), RcppRoll, RcppParallel, stats, stringi, utils, zoo
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: covr, doParallel, e1071, parallel, randomForest, testthat
Published: 2018-09-20
Author: David Ardia [aut], Keven Bluteau [aut], Samuel Borms [aut, cre], Kris Boudt [aut]
Maintainer: Samuel Borms <samuel.borms at unine.ch>
BugReports: https://github.com/sborms/sentometrics/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/sborms/sentometrics
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: sentometrics citation info
Materials: README NEWS
CRAN checks: sentometrics results

Downloads:

Reference manual: sentometrics.pdf
Package source: sentometrics_0.5.1.tar.gz
Windows binaries: r-devel: sentometrics_0.5.1.zip, r-release: sentometrics_0.5.1.zip, r-oldrel: sentometrics_0.4.zip
OS X binaries: r-release: sentometrics_0.5.1.tgz, r-oldrel: sentometrics_0.4.tgz
Old sources: sentometrics archive

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