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The R package sentometrics to compute, aggregate and predict with textual sentiment

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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
aggregation, penalized regression, prediction, R, sentometrics, textual sentiment, time series

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MLA
Ardia, David, et al. “The R Package Sentometrics to Compute, Aggregate and Predict with Textual Sentiment.” JOURNAL OF STATISTICAL SOFTWARE, vol. 99, no. 2, 2021, pp. 1–40, doi:10.18637/jss.v099.i02.
APA
Ardia, D., Bluteau, K., Borms, S., & Boudt, K. (2021). The R package sentometrics to compute, aggregate and predict with textual sentiment. JOURNAL OF STATISTICAL SOFTWARE, 99(2), 1–40. https://doi.org/10.18637/jss.v099.i02
Chicago author-date
Ardia, David, Keven Bluteau, Samuel Borms, and Kris Boudt. 2021. “The R Package Sentometrics to Compute, Aggregate and Predict with Textual Sentiment.” JOURNAL OF STATISTICAL SOFTWARE 99 (2): 1–40. https://doi.org/10.18637/jss.v099.i02.
Chicago author-date (all authors)
Ardia, David, Keven Bluteau, Samuel Borms, and Kris Boudt. 2021. “The R Package Sentometrics to Compute, Aggregate and Predict with Textual Sentiment.” JOURNAL OF STATISTICAL SOFTWARE 99 (2): 1–40. doi:10.18637/jss.v099.i02.
Vancouver
1.
Ardia D, Bluteau K, Borms S, Boudt K. The R package sentometrics to compute, aggregate and predict with textual sentiment. JOURNAL OF STATISTICAL SOFTWARE. 2021;99(2):1–40.
IEEE
[1]
D. Ardia, K. Bluteau, S. Borms, and K. Boudt, “The R package sentometrics to compute, aggregate and predict with textual sentiment,” JOURNAL OF STATISTICAL SOFTWARE, vol. 99, no. 2, pp. 1–40, 2021.
@article{8697290,
  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.}},
  author       = {{Ardia, David and Bluteau, Keven and Borms, Samuel and Boudt, Kris}},
  issn         = {{1548-7660}},
  journal      = {{JOURNAL OF STATISTICAL SOFTWARE}},
  keywords     = {{aggregation,penalized regression,prediction,R,sentometrics,textual sentiment,time series}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{1--40}},
  title        = {{The R package sentometrics to compute, aggregate and predict with textual sentiment}},
  url          = {{http://dx.doi.org/10.18637/jss.v099.i02}},
  volume       = {{99}},
  year         = {{2021}},
}

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