eMFDscore is a library for the fast and flexible extraction of various moral information metrics from textual input data. eMFDscore is built on spaCy for faster execution and performs minimal preprocessing consisting of tokenization, syntactic dependency parsing, lower-casing, and stopword/punctuation/whitespace removal. eMFDscore lets users score documents with multiple Moral Foundations Dictionaries, provides various metrics for analyzing moral information, and extracts moral patient, agent, and attribute words related to entities.
When using eMFDscore, please consider giving this repository a star (top right corner) and citing the following article:
Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., & Weber, R. (2020). The extended Moral Foundations Dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text. Behavior Research Methods, https://doi.org/10.3758/s13428-020-01433-0
eMFDscore is dual-licensed under GNU GENERAL PUBLIC LICENSE 3.0, which permits the non-commercial use, distribution, and modification of the eMFDscore package. Commercial use of the eMFDscore requires an application.
eMFDscore requires a Python installation (v3.7+). If your machine does not have Python installed, we recommend installing Python by downloading and installing either Anaconda or Miniconda for your OS.
For best practises, we recommend installing eMFDscore into a virtual conda environment. Hence, you should first create a virtual environment by executing the following command in your terminal:
$ conda create -n emfd python=3.7
Once Anaconda/Miniconda is installed activate the env via:
$ source activate emfd
Next, you must install spaCy, which is the main natural language processing backend that eMFDscore is built on:
$ conda install -c conda-forge spacy
$ python -m spacy download en_core_web_sm
Finally, you can install eMFDscore by copying, pasting, and executing the following command:
pip install https://github.com/medianeuroscience/emfdscore/archive/master.zip
eMFDscore can also be run in google colab. All you need to do is add these lines to the beginning of your notebook, execute them, and then restart your runtime:
!pip install -U pip setuptools wheel
!pip install -U spacy
!python -m spacy download en_core_web_sm
!pip install git+https://github.com/medianeuroscience/emfdscore.git
You can then use eMFDscore as a python library as documented in our tutorial.
Please refer to this tutorial to learn how to use eMFDscore.
If you are using the eMFD within the Global Database of Events, Language, and Tone (GDELT) please read the following documentation.
For using the eMFD on shorter texts (e.g., tweets and news headlines), we suggest to apply the eMFD in a FrameAxis.
The eMFD has been used in the following applications (ordered by date of publication):
- Harris, C., Myers, A., & Kaiser, A. (2022). Being Seen: How Markets Impact Our Moral Sentiments. Available at SSRN: https://ssrn.com/abstract=3997378 or http://dx.doi.org/10.2139/ssrn.3997378
- Malik, M., Hopp, F. R., Chen, Y., & Weber, R. (2021). Does Regional Variation in Pathogen Prevalence Predict the Moralization of Language in COVID-19 News? Journal of Language and Social Psychology.
- Chen, Kaiping, Zening Duan, and Sijia Yang. "Twitter as research data: Tools, costs, skill sets, and lessons learned." Politics and the Life Sciences (2021): 1-17.
- Van Vliet, L. (2021). Moral expressions in 280 characters or less: An Analysis of Politician tweets following the 2016 Brexit referendum vote. Frontiers in Big Data, 4, 49.
- Priniski, J. H., Mokhberian, N., Harandizadeh, B., Morstatter, F., Lerman, K., Lu, H., & Brantingham, P. J. (2021). Mapping Moral Valence of Tweets Following the Killing of George Floyd. arXiv preprint arXiv:2104.09578.
- Hopp, F. R., Fisher, J. T., & Weber, R. (2020). A graph-learning approach for detecting moral conflict in movie scripts. Media and Communication, 8(3), 164.