  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.
