Infer moral appeals from text
Project description
Introduction
vMFD
is an easy-to-use Python package for inferring moral appeals from text.
It extends eMFD through word embedding and yields superior performance.
Quick start
Install
After cloning this repo to your computer, go to the directory where setup.py
resides and use the following command to install the package:
pip install vMFD
Download data
The package relies on a pre-calculated data frame that needs to be downloaded. You can use the following command in a Python console or notebook:
import vMFD
vMFD.download_data("word_moral_appeals_googlenews")
You can replace word_moral_appeals_googlenews
with other categories. Currently, the following categories are supported:
Category name | Note |
---|---|
word_moral_appeals_googlenews | Based on pre-trained embedding by Google. The embedding contains 300-dimensional vectors for 3 million words and phrases. See https://code.google.com/archive/p/word2vec/ for details. |
Calculate moral intuitions
Once the data is downloaded, you can calculate the moral intuitions of any text.
import vMFD
vo = vMFD.vMFD()
# Only calculate the valence
vo.calculate_valence("Trump is the best president")
# Calculate all metrics
vo.calculate_metrics("Trump is the best president")
Project details
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