Basic usage script for dictionary-based sentiment analysis. Intended use with labMT data
TL;DR a simple labMT usage script
This script uses the language assessment by Mechanical Turk (labMT) word list to score the happiness of a corpus. The labMT word list was created by combining the 5000 words most frequently appearing in four sources: Twitter, the New York Times, Google Books, and music lyrics, and then scoring the words for sentiment on Amazon’s Mechanical Turk. The list is described in detail in the publication Dodds’ et al. 2011, PLOS ONE, “Temporal Patterns of Happiness and Information in a Global-Scale Social Network: Hedonometrics and Twitter.”
Given two corpora, the script “storylab.py” creates a word-shift graph illustrating the words most responsible for the difference in happiness between the two corpora. The corpora should be large (e.g. at least 10,000 words) in order for the difference to be meaningful, as this is a bag-of-words approach. As an example, a random collection of English tweets from both Saturday January 18 2014 and Tuesday January 21 2014 are included in the “example” directory. They can be compared by moving to the test directory, using the command
python example.py example-shift.html
and opening the file example-shift.html in a web browser. For an explanation of the resulting plot, please visit
Cloning the github directly is recommended, i.e.
git clone https://github.com/andyreagan/labMT-simple.git
and then installing locally using
sudo python setup.py install
Tests can be run by navigating to the test directory, and running
which will compare the two days in test/data and print test.html which shifts them, allowing for a changable lens.
This repository can also be installed using pip
pip install labMTsimple
in which case you can download the tests from github and run them, if desired.