Research material & open-source software by and for the community

In a nutshell

Within the growing and fascinating landscape at the frontier of text mining, sentiment analysis, and econometrics, the field sentometrics has emerged. Researchers in sentometrics investigate the transformation of qualitative sentiment embedded in textual data (and other alternative data sources) into quantitative sentiment variables, and their subsequent application in an econometric analysis of the relationships between sentiment and other variables.

Many researchers steer forward sentometrics by doing tremendous work across the domains of economics, finance, politics and beyond. The objective of this hub is to provide resources and open-source software to help the community of these researchers interact with each other and showcase their work, while also introducing those interested to enter the field.

This survey paper and the R package sentometrics are perfect starting points to dive into this exciting field.

Live Indices

Thanks to a partnership with news agencies, daily updated indices are provided on the satellite website. Note that the live indices on the satellite website may differ from those used in some of our academic research. The indices arising from academic research papers, which are in most cases not (or infrequently) updated, are available in the Data section of this website.


EPU Belgium

Daily EPU Flanders, Wallonia, and Belgium updated daily from 2003 to today.


Daily U.S. Media Climate Change Concerns Index from 2003 to 2018.

U.S Topical Economic Sentiment

Daily Topical U.S Economic Sentiment Indices from 1996 to 2016.

EPU Quebec

Monthly EPU from French-Canadian sources. Index available from 1913 to 2020.


Café Scientifique IVADO

David Ardia presented recent research projects at Café Scientifique IVADO. Thanks to Alaa Kassem, Thien Duy Tran, and Clément Aymard for their participation!

Webinar at Ran.Données 2022

David Ardia presented a one-hour webinar about Science ouverte at the IVADO Ran.Données 2022. Slides are available here!




Miscellaneous functions for training and plotting classification and regression models.


LASSO and elastic net regularized generalized linear models.


Sentiment lexicon calibration with the Generalized Word Power methodology.


NLTK is a leading platform for building Python programs to work with human language data.


A fast, flexible, and comprehensive framework for quantitative text analysis in R.


Machine learning in Python.


Dictionary-based sentiment analysis.


An integrated framework for textual sentiment time series aggregation and prediction.

A Shiny interface to the R package sentometrics.


Tools for estimating and analyzing various classes of sentiment/topic models.


Industrial-strength natural language processing in Python.


The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates.


TextBlob is a Python library for processing textual data.


Inverse regression analysis of text.


Text mining using tidy tools.


State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0.


Natural language processing toolkit.


Sentiment analysis tool that is specifically attuned to sentiments expressed in social media.


You can contribute by submitting a resource using this form. Please include what type of resource (index, post, software, publication) as well as a link to the resource and we will get in touch!


HEC Montreal

Research professorship in Sentometrics

Team Up

Grant for academic research & industry collaboration

Technology Transfer

Research spin-off