The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment

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.

Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence

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The power of text-based indicators in forecasting Italian economic activity

“…Our sentiment indicators are computed using the R package Sentometrics (Ardia et al, 2017), which allows fast sentiment computations (see figure A.2 in the Appendix for a schematic pipeline of the procedure).…”

“…We use a multi-source database of Italian newspaper articles related to economic news to build a set of sentiment and uncertainty measures along the lines put forward by Soroka (2006), Tetlock (2007), Ardia et al (2017), Loughran and McDonald (2011), Loughran and McDonald (2014), Baker et al (2016) and then we use these indicators to nowcast and forecast the Italian real economic activity.…”

Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R

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