vMFD 0.2

Infer moral appeals from text

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")