🕵️‍♀️ Variationist is a highly-modular, flexible, and customizable tool to analyze and explore language variation and bias in written language data. It allows researchers, from NLP practitioners to linguists and social scientists, to seamlessly investigate language use across many dimensions and a wide range of use cases.

Alan Ramponi, Camilla Casula and Stefano Menini. 2024. Variationist: Exploring Multifaceted Variation and Bias in Written Language Data. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 346–354, Bangkok, Thailand. ACL. [cite] [paper]

• 💿 Installation
• 🏁 Quickstart
• 📕 Tutorials
• 📖 Documentation
• 📹 Video
• ✈️ Roadmap
• 🌀 Contributors
• ✏️ Citation

Installation

Python package

🕵️‍♀️ Variationist can be installed as a python package from PyPI using the pip command as follows:

pip install variationist

Installing from source

Alternatively, 🕵️‍♀️ Variationist can be installed from source as follows:

1. Clone this repository on your own path:

git clone https://github.com/dhfbk/variationist.git

2. Create an environment with your own preferred package manager. We used python 3.9 and dependencies listed in requirements.txt. If you use conda, you can just run the following commands from the root of the project:

conda create --name variationist python=3.9         # create the environment
conda activate variationist                         # activate the environment
pip install --user -r requirements.txt              # install the required packages

Quickstart

🕵️‍♀️ Variationist works in a few line of codes and supports a wide variety of use cases in many dimensions. Below is an introductory example on how it can be used to explore variation and bias on a very simple dataset with a single text column and just a variable.

1) Import 🕵️‍♀️ Variationist

We first import the main classes useful for computation and visualization as follows:

from variationist import Inspector, InspectorArgs, Visualizer, VisualizerArgs

A brief description for the classes is the following:

• Inspector (and InspectorArgs): it takes care of orchestrating the analysis, from importing and tokenizing the data to calculating the metrics and creating outputs with all the calculated metrics for each text column, variable, and combination thereof. It relies on InspectorArgs, a dataclass that allows the user to specify a variety of arguments that relate to the analysis.
• Visualizer (and VisualizerArgs): it takes care of orchestrating the creation of a variety of interactive charts showing up to five dimensions based on the results and metadata from a prior analysis using Inspector. It relies on VisualizerArgs, a class storing the specific arguments for visualization.

2) Define and run the Inspector

Now, we aim to inspect the data. For this example, we use a column text and just a single label variable (with a default nominal variable type and a default general variable semantics); however, note that 🕵️‍♀️ Variationist can seamlessly handle a potentially unlimited number of variables and up to two text columns during computation. We just use npw_pmi as our association metric and rely on single tokens as our unit of information, using a default tokenizer. We also ask for some preprocessing steps (stopwords removal in English and lowercasing). The output is stored in the results variable but it can alternatively be serialized to a .json file for later use.

# Define the inspector arguments
ins_args = InspectorArgs(text_names=["text"], var_names=["label"], 
    metrics=["npw_pmi"], n_tokens=1, language="en", stopwords=True, lowercase=True)

# Run the inspector and get the results
res = Inspector(dataset="data.tsv", args=ins_args).inspect()

3) Define and run the Visualizer

Finally, we aim to visualize the results. The visualizer currently handles the creation of interactive charts for more than 30 combinations of variable type and semantics up to five dimensions, in which two of them are naturally fixed: the units (nominal) and their metric scores (quantitative). For this example, we output in the output folder my_charts the results in a html format (i.e., the default and suggested one for the sake of interactivity).

# Define the visualizer arguments
vis_args = VisualizerArgs(output_folder="charts", output_formats=["html"])

# Create interactive charts for all metrics
charts = Visualizer(input_json=res, args=vis_args).create()

Optionally, interactive charts can be visualized in notebooks by just taking the object returned from the create() function. For instance, if the object is stored in a variable named charts, visualization would be as simple as writing the following string in the notebook: charts[$METRIC][$CHART_TYPE], where $METRIC is the metric of interest and $CHART_TYPE is a specific chart type associated with that metric.

Tutorials

You can find our tutorials to learn how to better leverage 🕵️‍♀️ Variationist in the examples/ folder.

There you can also find a set of interesting case studies using real-world datasets! 📈

Documentation

You can find more information on specific topics in the following documents:

• Input dataset: from .tsv or .csv files to pandas dataframes and Hugging Face datasets
• Units: from tokens and n-grams to co-occurrences with windows and duplicate handling
• Tokenizers: from a whitespace tokenizer to Hugging Face tokenizers and custom ones
• Variables: possible variable types and variable semantics, and their interdependence
• Metrics: from basic statistics to lexical diversity, association metrics, and custom ones
• Charts: from scatter charts to choroplets, from heatmaps to temporal line plots and others
• Custom components: how to define your own components

A technical documentation for 🕵️‍♀️ Variationist is also available at: https://variationist.readthedocs.io/en/latest/.

Video

An short introductory video is available here.

Roadmap

🕵️‍♀️ Variationist aims to be as accessible as possible to researchers from a wide range of fields. We thus aim to provide the following features in the next releases:

• An easy to use graphical user interface to be installed locally or used through Hugging Face Spaces;
• Extension of the unit concept to also cover linguistic aspects beyond the lexical level.

Contributors

• Alan Ramponi, Fondazione Bruno Kessler
• Camilla Casula, Fondazione Bruno Kessler and University of Trento
• Stefano Menini, Fondazione Bruno Kessler

Citation

If you use 🕵️‍♀️ Variationist in your work, please cite our paper as follows:

@inproceedings{ramponi-etal-2024-variationist,
    title = "Variationist: Exploring Multifaceted Variation and Bias in Written Language Data",
    author = "Ramponi, Alan and Casula, Camilla and Menini, Stefano",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.33",
    pages = "346--354"
}