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                <text>We provide a hands-on introduction to optimized textual sentiment indexation using&#13;
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the potential information value of textual data. The sentometrics package implements an&#13;
intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate&#13;
the scores into multiple time series, and to use these time series to predict other variables.&#13;
The workflow of the package is illustrated with a built-in corpus of news articles from two&#13;
major U.S. journals to forecast the CBOE Volatility Index</text>
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                <text>HEC Montréal&#13;
GERAD</text>
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custom user-defined optimality criteria, N-level split-plot designs, mixture designs, and&#13;
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                <text>Institute for Defense Analyses</text>
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