Package: sentometrics 1.0.0

Samuel Borms

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. (2021) <doi:10.18637/jss.v099.i02>.

Authors:Samuel Borms [aut, cre], David Ardia [aut], Keven Bluteau [aut], Kris Boudt [aut], Jeroen Van Pelt [ctb], Andres Algaba [ctb]

sentometrics_1.0.0.tar.gz
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sentometrics_1.0.0.tgz(r-4.4-emscripten)sentometrics_1.0.0.tgz(r-4.3-emscripten)
sentometrics.pdf |sentometrics.html✨
sentometrics/json (API)
NEWS

# Install 'sentometrics' in R:
install.packages('sentometrics', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sentometricsresearch/sentometrics/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

2.39 score 49 scripts 527 downloads 24 exports 89 dependencies

Last updated 3 years agofrom:f910ca1edf. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 16 2024
R-4.5-linux-x86_64NOTEOct 16 2024

Exports:add_featuresas.sentimentas.sento_corpusattributionscompute_sentimentcorpus_summarizectr_aggctr_modelget_datesget_dimensionsget_howsget_loss_datameasures_fillmeasures_updatenmeasurespeakdatespeakdocssento_corpussento_lexiconssento_measuressento_modelweights_almonweights_betaweights_exponential

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverfastmatchforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowergtablehardhatipredisobandISOcodesISOweekiteratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrquantedaR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppRollrecipesreshape2rlangrpartscalesshapeSnowballCSQUAREMstopwordsstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxml2yaml

Readme and manuals

Help Manual

Help pageTopics
sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Predictionsentometrics-package sentometrics
Add feature columns to a (sento_)corpus objectadd_features
Aggregate textual sentiment across sentences, documents and timeaggregate.sentiment
Aggregate sentiment measuresaggregate.sento_measures
Get the sentiment measuresas.data.table.sento_measures
Convert a sentiment table to a sentiment objectas.sentiment
Convert a quanteda or tm corpus object into a sento_corpus objectas.sento_corpus
Retrieve top-down model sentiment attributionsattributions
Compute textual sentiment across features and lexiconscompute_sentiment
Summarize the sento_corpus objectcorpus_summarize
Set up control for aggregation into sentiment measuresctr_agg
Set up control for sentiment-based sparse regression modelingctr_model
Differencing of sentiment measuresdiff.sento_measures
Monthly U.S. Economic Policy Uncertainty indexepu
Get the dates of the sentiment measures/time seriesget_dates
Get the dimensions of the sentiment measuresget_dimensions
Options supported to perform aggregation into sentiment measuresget_hows
Retrieve loss data from a selection of modelsget_loss_data
Built-in lexiconslist_lexicons
Built-in valence word listslist_valence_shifters
Add and fill missing dates to sentiment measuresmeasures_fill
Update sentiment measuresmeasures_update
Merge sentiment objects horizontally and/or verticallymerge.sentiment
Get number of sentiment measuresnmeasures
Get number of observations in the sentiment measuresnobs.sento_measures
Extract dates related to sentiment time series peakspeakdates
Extract documents related to sentiment peakspeakdocs
Plot prediction attributions at specified levelplot.attributions
Plot sentiment measuresplot.sento_measures
Plot iterative predictions versus realized valuesplot.sento_modelIter
Make predictions from a sento_model objectpredict.sento_model
Scaling and centering of sentiment measuresscale.sento_measures
Create a sento_corpus objectsento_corpus
Set up lexicons (and valence word list) for use in sentiment analysissento_lexicons
One-way road towards a sento_measures objectsento_measures
Optimized and automated sentiment-based sparse regressionsento_model
Subset sentiment measuressubset.sento_measures
Texts (not) relevant to the U.S. economyusnews
Compute Almon polynomialsweights_almon
Compute Beta weighting curvesweights_beta
Compute exponential weighting curvesweights_exponential