sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction (original) (raw)
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 |
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