The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment
Dublin Core
Title
The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment
Subject
aggregation, penalized regression, prediction, R, sentometrics, textual sentiment,
time series
time series
Description
We provide a hands-on introduction to optimized textual sentiment indexation using
the R package sentometrics. Textual sentiment analysis is increasingly used to unlock
the potential information value of textual data. The sentometrics package implements an
intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate
the scores into multiple time series, and to use these time series to predict other variables.
The workflow of the package is illustrated with a built-in corpus of news articles from two
major U.S. journals to forecast the CBOE Volatility Index
the R package sentometrics. Textual sentiment analysis is increasingly used to unlock
the potential information value of textual data. The sentometrics package implements an
intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate
the scores into multiple time series, and to use these time series to predict other variables.
The workflow of the package is illustrated with a built-in corpus of news articles from two
major U.S. journals to forecast the CBOE Volatility Index
Creator
David Ardia
Source
https://www.jstatsoft.org/article/view/v099i02
Publisher
HEC Montréal
GERAD
GERAD
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
David Ardia, “The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8203.