PicaultRenault_data

A regression dataset built to partially replicate the result of
Picault &amp; Renault. This dataset contains, for each press conference
published after 2000:<ul>
<li>The Main Refinancing Rate (MRR) of the ECB set following the press
conference</li>
<li>The change in the MRR following the press conference</li>
<li>The change in the MRR observed at the previous press conference</li>
<li>The Bloomberg consensus on the announced MRR</li>
<li>The Surprise brought by the announcement, computed as the Bloomberg
consensus minus the MRR following the conference</li>
<li>The EURO STOXX 50 return on the day of the press conference</li>
<li>The EURO STOXX 50 return on the day preceding the announcement</li>
</ul>

datasets

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

PicaultRenault_data function

An <a href="/link/xts%3A%3Axts?package=sentopics&version=0.7.4" data-mini-rdoc="sentopics::xts::xts">xts::xts</a> object.

Format

Regression dataset based on Picault &amp; Renault (2017) — PicaultRenault_data

An <a href='https://rdrr.io/pkg/xts/man/xts.html'>xts::xts</a> object.

PicaultRenault_data: Regression dataset based on Picault & Renault (2017)

Description

Usage

Arguments

Format

References

Examples