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An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction
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README.md

sentometrics: An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

sentometrics

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Introduction

The sentometrics package is an integrated framework for textual sentiment time series aggregation and prediction. It accounts for the intrinsic challenge that, for a given text, sentiment can be computed in many different ways, as well as the large number of possibilities to pool sentiment across texts and time. This additional layer of manipulation does not exist in standard text mining and time series analysis packages. The package therefore integrates the fast qualification of sentiment from texts, the aggregation into different sentiment time series and the optimized prediction based on these measures.

See the project page, the vignette and following paper for respectively a brief and an extensive introduction to the package, and a real-life macroeconomic forecasting application.

Installation

To install the package from CRAN, simply do:

install.packages("sentometrics")

The latest development version of sentometrics is available at https://github.com/sborms/sentometrics. To install this version (which may contain bugs!), execute:

devtools::install_github("sborms/sentometrics")

References

Please cite sentometrics in publications. Use citation("sentometrics").

Acknowledgements

This software package originates from a Google Summer of Code 2017 project.

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