emfd 0.1.0

Extended Moral Foundation Dictionary Scoring

A forked emfd from emfdscore

Improvements

• compatible with latest spacy and pandas package

• score_docs using column index for scoring

• original one: emfdscore

eMFDscore: Extended Moral Foundation Dictionary Scoring for Python

emfd is a library for the fast and flexible extraction of various moral information metrics from textual input data. emfd 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.

Before install

install spacy and en_core_web_sm

Install

pip install emfd

Usage

• emfd

import pandas as pd
from emfdscore.scoring import score_docs
df = pd.read_csv("your_data_set.csv")
num_docs = len(df)
DICT_TYPE = 'emfd'
PROB_MAP = 'all' # or single
SCORE_METHOD = 'bow' # or more options
OUT_METRICS = 'sentiment' # or vice-virtue
OUT_CSV_PATH = './your_output.csv'
df = score_docs(df,DICT_TYPE,PROB_MAP,SCORE_METHOD,OUT_METRICS,num_docs)
df.to_csv(OUT_CSV_PATH, index=False)

Future

• polars implementations
• vectorized operations

eMFDscore in Google Colaboratory

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 emfd

Usage

Applications

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.