compute_PicaultRenault_scores

Computes Monetary Policy and Economic Condition scores using the
Picault-Renault lexicon for central bank communication.

A framework that joins topic modeling and sentiment analysis of
textual data. The package implements a fast Gibbs sampling estimation of
Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
<doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
helpers and visualizations to analyze the result of topic modeling. The
framework also allows enriching topic models with dates and externally
computed sentiment measures. A flexible aggregation scheme enables the
creation of time series of sentiment or topical proportions from the enriched
topic models. Moreover, a novel method jointly aggregates topic proportions
and sentiment measures to derive time series of topical sentiment.

Olivier Delmarcelle

sentopics

Tools for Joint Sentiment and Topic Analysis of Textual Data

Samuel Borms

Chengua Lin

Yulan He

Jose Bernardo

David Robinson

Julia Silge

compute_PicaultRenault_scores function

<dl><dt>x</dt>
<dd>a quanteda::corpus object.</dd>
<dt>min_ngram</dt>
<dd>the minimum length of n-grams considered in the computation</dd>
<dt>return_dfm</dt>
<dd>if <code>TRUE</code>, returns the scaled word-per-document score under
two quanteda::dfm, on for the Monetary Policy and one for the Economic Condition
categories. If <code>FALSE</code>, returns the sum of all word scores per document.</dd></dl>

Arguments

Compute scores using the Picault-Renault lexicon — compute_PicaultRenault_scores

<dl>

<dt>x</dt>
<dd>a quanteda::corpus object.</dd>


<dt>min_ngram</dt>
<dd>the minimum length of n-grams considered in the computation</dd>


<dt>return_dfm</dt>
<dd>if <code>TRUE</code>, returns the scaled word-per-document score under
two quanteda::dfm, on for the Monetary Policy and one for the Economic Condition
categories. If <code>FALSE</code>, returns the sum of all word scores per document.</dd>

</dl>

compute_PicaultRenault_scores: Compute scores using the Picault-Renault lexicon

Description

Usage

Value

Arguments

Details

References

See Also

Examples