The R package sentometrics to compute, aggregate and predict with textual sentiment
Machine Learning
2021-10-22 v1 Computation and Language
Machine Learning
Applications
Abstract
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.
Keywords
Cite
@article{arxiv.2110.10817,
title = {The R package sentometrics to compute, aggregate and predict with textual sentiment},
author = {David Ardia and Keven Bluteau and Samuel Borms and Kris Boudt},
journal= {arXiv preprint arXiv:2110.10817},
year = {2021}
}