Beautiful visualizations of how language differs among document types.

A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).

Text analysis with networks.

Interpretable data visualizations for understanding how texts differ at the word level

Notebooks for the Seattle PyData 2017 talk on Scattertext

Summer/ winter schools, workshops and conferences in computational social science 🫂

A tool for Semantic Scaling of Political Text (branch of Topfish, a suite of tools for Political Text Analysis)

Literature 📄 and datasets 📚 on automatic populism detection

2018 Computational Text Analysis Notebooks, University of Mannheim

Code and models for 3 different tools to measure appeals to 8 discrete emotions in German political text

Summer 2017 Social Media Analytics Workshop Series

LinkOrgs: An R package for linking linking records on organizations using half a billion open-collaborated records from LinkedIn

An Automation Webcrawler for Extracting Central Bankers' Speeches

'dictvectoR' measures the similarity between a concept dictionary and documents, using fastText word vectors. Implements the "Distributed-Dictionary-Representation" (Garten et al. 2018) method in R.

The ABC of Computational Text Analysis. BA Seminar, Spring 2022, University of Lucerne

A small showcase for topic modeling with the tmtoolkit Python package. I use a corpus of articles from the German online news website Spiegel Online (SPON) to create a topic model for before and during the COVID-19 pandemic.

Code for estimating word embeddings with gensim in R.

From using xpdf, rvest, and quanteda on United Nations Digital Library search results to applying dictionaries to speeches in United Nations meeting records

A tutorial on using regular expressions in R

Empirical framework applied to parliament discourses and Twitter data, with a Discourse Polarization Index.