The
sentometricspackage offers an integrated framework for textual sentiment time series aggregation and prediction. It accounts for the intrinsic challenge that textual sentiment can be computed in many different ways, as well as the large number of possibilities to pool sentiment into a time series index. The package integrates the fast quantification of sentiment from texts, the aggregation into different sentiment time series, and the prediction based on these measures. All in one coherent workflow!
Explore this package website to learn about what you can do with sentometrics and how so.
Please cite sentometrics in publications. See the Citation section on the right.
This software package originates from a Google Summer of Code 2017 project, was further developed during a follow-up Google Summer of Code 2019 project, and benefited generally from financial support by Innoviris, IVADO, swissuniversities, and the Swiss National Science Foundation (grants #179281 and #191730).
Reach out to Samuel Borms if you have questions, suggestions or want to become a contributor. See the News > Development section to find out what you can help us with.