Package: sentometrics
Type: Package
Title: An Integrated Framework for Textual Sentiment Time Series
        Aggregation and Prediction
Version: 0.2
Authors@R: c(person("David", "Ardia", email = "david.ardia@unine.ch", role = c("aut")),
  person("Keven", "Bluteau", email = "keven.bluteau@unine.ch", role = c("aut")),
  person("Samuel", "Borms", email = "samuel.borms@unine.ch", role = c("aut", "cre")),
  person("Kris", "Boudt", email = "kris.boudt@vub.be", role = c("aut")))
Author: David Ardia [aut],
  Keven Bluteau [aut],
  Samuel Borms [aut, cre],
  Kris Boudt [aut]
Maintainer: Samuel Borms <samuel.borms@unine.ch>
Description: Time series analysis based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in many ways. As described in Ardia et al. (2017) <https://ssrn.com/abstract=3067734>, the package provides a means to model the impact of sentiment in texts on a target variable, by first computing a wide range of textual sentiment measures and then selecting those that are most informative.
Depends: R (>= 3.4.2), data.table, ggplot2, foreach
License: GPL (>= 2)
BugReports: https://github.com/sborms/sentometrics/issues
URL: https://github.com/sborms/sentometrics
Encoding: UTF-8
LazyData: true
Suggests: testthat, e1071, randomForest
Imports: utils, stats, quanteda, sentimentr, stringi, zoo, abind,
        glmnet, caret, compiler, Rcpp (>= 0.12.13), RcppRoll, ggthemes,
        ISOweek, MCS
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2017-11-12 21:50:05 UTC; gebruiker
Repository: CRAN
Date/Publication: 2017-11-13 10:34:52 UTC
