predict.sento_model

Prediction method for <code>sento_model</code> class, with usage along the lines of
<code>predict.glmnet</code>, but simplified in terms of parameters.

Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.

Samuel Borms

sentometrics

An Integrated Framework for Textual Sentiment Time Series
Aggregation and Prediction

David Ardia

Keven Bluteau

Kris Boudt

Jeroen Van Pelt

Andres Algaba

predict.sento_model function

<dl><dt>object</dt>
<dd>a <code>sento_model</code> object created with <code>sento_model</code>.</dd>
<dt>newx</dt>
<dd>a data <code>matrix</code> used for the prediction(s), row-by-row; see
<code>predict.glmnet</code>. The number of columns should be equal to <code>sum(sento_model$nVar)</code>, being the
number of original sentiment measures and other variables. The variables discarded in the regression process are
dealt with within this function, based on <code>sento_model$discarded</code>.</dd>
<dt>type</dt>
<dd>type of prediction required, a value from <code>c("link", "response", "class")</code>, see documentation for
<code>predict.glmnet</code>.</dd>
<dt>offset</dt>
<dd>not used.</dd>
<dt>...</dt>
<dd>not used.</dd></dl>

Arguments

Author

Make predictions from a sento_model object — predict.sento_model

<dl>

<dt>object</dt>
<dd>a <code>sento_model</code> object created with <code>sento_model</code>.</dd>


<dt>newx</dt>
<dd>a data <code>matrix</code> used for the prediction(s), row-by-row; see
<code>predict.glmnet</code>. The number of columns should be equal to <code>sum(sento_model$nVar)</code>, being the
number of original sentiment measures and other variables. The variables discarded in the regression process are
dealt with within this function, based on <code>sento_model$discarded</code>.</dd>


<dt>type</dt>
<dd>type of prediction required, a value from <code>c("link", "response", "class")</code>, see documentation for
<code>predict.glmnet</code>.</dd>


<dt>offset</dt>
<dd>not used.</dd>


<dt>...</dt>
<dd>not used.</dd>

</dl>

predict.sento_model: Make predictions from a sento_model object

Description

Usage

Value

Arguments

Author

See Also