CTApy 0.1.2

Python package for the Conditional Topic Allocation (CTA)

CTApy

Python package for the "Conditional Topic Allocation" (CTA): a text-analysis method that identifies topics that correlate with numerical outcomes.

• Corresponding research paper: Conditional Topic Allocations for Open-Ended Survey Responses (2024).

How does CTA work?

CTA finds topics by conditioning on observables. For example, do Republicans write differently about politics than Democrats? It consists of three steps:

1. Predict the outcome variable with text.

• Use DistillBERT to predict outcome.

2. Select words with high predictive power (positive or negative).

• Calculate SHAP values for each word and select words with a statistically significant SHAP value.

3. Group words by semantic similarity.

• Topics with either positive or negative correlation with the outcome.

CTA supports all languages.

Installation

Runs on Windows and requires Python 3.9 and pip.
It is highly recommended to use a virtual environment (or conda environment) for the installation.

# upgrade pip, wheel and setuptools
python -m pip install -U pip wheel setuptools

# install the package
python -m pip install -U CTApy

If you want to use Jupyter, make sure you have it installed in the current environment.

Quickstart

Please see the hands-on tutorials, which replicate the research paper: https://github.com/twekhof/CTA/tutorials. The paper uses the following package versions:
-torch: 2.4.0
-transformers: 4.32.1

Author

CTApy was developed by

Tobias Wekhof, ETH Zurich

Disclaimer

This Python package is a research tool currently under development. The authors take no responsibility for the accuracy or reliability of the results produced by it.